Applications of NMR spectroscopy to systems biochemistry

NMR-Based Stable Isotope Tracing of Cancer Metabolism

NMR is widely used for metabolite profiling (metabolomics, metabonomics) particularly of various readily obtainable biofluids such as plasma and urine. It is especially valuable for stable isotope tracer studies to track metabolic pathways under control or perturbed conditions in a wide range of cell models as well as animal models and human subjects. NMR has unique properties for utilizing stable isotopes to edit or simplify otherwise complex spectra acquired in vitro and in vivo, while quantifying the level of enrichment at specific atomic positions in various metabolites (i.e., isotopomer distribution analysis).In this protocol, we give an overview with specific protocols for NMR-based stable isotope-resolved metabolomics, or SIRM, with a workflow from administration of isotope-enriched precursors, via sample preparation through to NMR data collection and reduction. We focus on indirect detection of common NMR-active stable isotopes including 13C, 15N, 31P, and 2H, using a variety of 1H-based two-dimensional experiments. We also include the application and analyses of multiplex tracer experiments.

Differentiating leukemia subtypes based on metabolic signatures using hyperpolarized 13C NMR

Leukemia is a group of blood cancers that are classified in four major classes. Within these four classes, many different subtypes exists with similar origin, genetic mutations, and level of maturity, which can make them difficult to distinguish. Despite their similarities, they might respond differently to treatment, and therefore distinguishing between them is of crucial importance. A deranged metabolic phenotype (Warburg effect) is often seen in cancer cells, leukemia cells included, and is increasingly a target for improved diagnosis and treatment. In this study, hyperpolarized 13C NMR spectroscopy was used to characterize the metabolic signatures of the six leukemia cell lines ML-1, CCRF-CEM, THP-1, MOLT-4, HL-60, and K562. This was done using [1-13C]pyruvate and [1-13C]alanine as bioprobes for downstream metabolite quantification and kinetic analysis on cultured cells with and without 2-deoxy-D-glucose treatment. The metabolic signatures of similar leukemia subtypes could be readily distinguished. This includes ML-1 and THP-1, which are of the similar M4 and M5 AML subtypes, CCRF-CEM and MOLT-4, which are of the similar T-ALL lineage at different maturation states, and HL-60 and K562, which are of the closely related M1 and M2 AML subtypes. The data presented here demonstrate the potential of hyperpolarized 13C NMR spectroscopy as a method to differentiate between leukemia subtypes of similar origin. Combining this method with bioreactor setups could potentially allow for better leukemia disease management as metabolic signatures could be acquired from a single biopsy through repeated experimentation and intervention.

Discrimination of serum samples of prostate cancer and benign prostatic hyperplasia with 1H-NMR metabolomics

Prostate cancer continues to be a prominent health concern for men globally. Current screening techniques, primarily the prostate-specific antigen (PSA) test and digital rectal examination (DRE), possess inherent limitations, with prostate biopsy being the definitive diagnostic procedure. The invasive nature of the biopsy and other drawbacks of current screening tests create the need for non-invasive and more accurate diagnostic methods. This study utilized 1H-NMR (Proton Nuclear Magnetic Resonance) based serum metabolomics to differentiate between prostate cancer (PCa) and benign prostatic hyperplasia (BPH). Serum samples from 40 PCa and 41 BPH patients were analysed using 1H-NMR spectroscopy. PepsNMR was utilized for preprocessing the raw NMR data, and the binned spectra were examined for patterns distinguishing PCa and BPH. Principal component analysis (PCA) showed a moderate separation between PCa and BPH, highlighting the distinct metabolic profiles of both conditions. A logistic regression model was then developed, which demonstrated good performance in distinguishing between the two conditions. The results showed significant variance in multiple metabolites between PCa and BPH, such as isovaleric acid, ethylmalonic acid, formate, and glutamic acid. This research underlines the potential of 1H-NMR-based serum metabolomics as a promising tool for improved prostate cancer screening, offering an alternative to the limitations of current screening methods.

Glycosylation: mechanisms, biological functions and clinical implications

Protein post-translational modification (PTM) is a covalent process that occurs in proteins during or after translation through the addition or removal of one or more functional groups, and has a profound effect on protein function. Glycosylation is one of the most common PTMs, in which polysaccharides are transferred to specific amino acid residues in proteins by glycosyltransferases. A growing body of evidence suggests that glycosylation is essential for the unfolding of various functional activities in organisms, such as playing a key role in the regulation of protein function, cell adhesion and immune escape. Aberrant glycosylation is also closely associated with the development of various diseases. Abnormal glycosylation patterns are closely linked to the emergence of various health conditions, including cancer, inflammation, autoimmune disorders, and several other diseases. However, the underlying composition and structure of the glycosylated residues have not been determined. It is imperative to fully understand the internal structure and differential expression of glycosylation, and to incorporate advanced detection technologies to keep the knowledge advancing. Investigations on the clinical applications of glycosylation focused on sensitive and promising biomarkers, development of more effective small molecule targeted drugs and emerging vaccines. These studies provide a new area for novel therapeutic strategies based on glycosylation.

Challenges of Spatially Resolved Metabolism in Cancer Research

Stable isotope-resolved metabolomics comprises a critical set of technologies that can be applied to a wide variety of systems, from isolated cells to whole organisms, to define metabolic pathway usage and responses to perturbations such as drugs or mutations, as well as providing the basis for flux analysis. As the diversity of stable isotope-enriched compounds is very high, and with newer approaches to multiplexing, the coverage of metabolism is now very extensive. However, as the complexity of the model increases, including more kinds of interacting cell types and interorgan communication, the analytical complexity also increases. Further, as studies move further into spatially resolved biology, new technical problems have to be overcome owing to the small number of analytes present in the confines of a single cell or cell compartment. Here, we review the overall goals and solutions made possible by stable isotope tracing and their applications to models of increasing complexity. Finally, we discuss progress and outstanding difficulties in high-resolution spatially resolved tracer-based metabolic studies.

Analysis of the metabolic profile of humans naturally exposed to RF-EM radiation

INTRODUCTION

The world is experiencing exponential growth in communication, especially wireless communication. Wireless connectivity has recently become a part of everyone's daily life. Recent developments in low-cost, low-power, and miniature devices contribute to a significant rise in radiofrequency-electromagnetic field (RF-EM) radiation exposure in our environment, raising concern over its effect on biological systems. The inconsistent and conflicting research results make it difficult to draw definite conclusions about how RF-EM radiation affects living things.

OBJECTIVES

This study identified two micro-environments based on their level of exposure to cellular RF-EM radiation, one with significantly less exposure and another with very high exposure to RF-EM radiation. Emphasis is given to studying the metabolites in the urine samples of humans naturally exposed to these two different microenvironments to understand short-term metabolic dysregulations.

METHODS

Untargeted 1H NMR spectroscopy was employed for metabolomics analyses to identify dysregulated metabolites. A total of 60 subjects were recruited with 5 ml urine samples each. These subjects were divided into two groups: one highly exposed to RF-EM (n = 30) and the other consisting of low-exposure populations (n = 30).

RESULTS

The study found that the twenty-nine metabolites were dysregulated. Among them, 19 were downregulated, and 10 were upregulated. In particular, Glyoxylate and dicarboxylate and the TCA cycle metabolism pathway have been perturbed. The dysregulated metabolites were validated using the ROC curve analysis.

CONCLUSION

Untargeted urine metabolomics was conducted to identify dysregulated metabolites linked to RF-EM radiation exposure. Preliminary findings suggest a connection between oxidative stress and gut microbiota imbalance. However, further research is needed to validate these biomarkers and understand the effects of RF-EM radiation on human health. Further research is needed with a diverse population.

NMR spectroscopy for metabolomics in the living system: recent progress and future challenges

Metabolism is a fundamental process that underlies human health and diseases. Nuclear magnetic resonance (NMR) techniques offer a powerful approach to identify metabolic processes and track the flux of metabolites at the molecular level in living systems. An in vitro study through in-cell NMR tracks metabolites in real time and investigates protein structures and dynamics in a state close to their most natural environment. This technique characterizes metabolites and proteins involved in metabolic pathways in prokaryotic and eukaryotic cells. In vivo magnetic resonance spectroscopy (MRS) enables whole-organism metabolic monitoring by visualizing the spatial distribution of metabolites and targeted proteins. One limitation of these NMR techniques is the sensitivity, for which a possible improved approach is through isotopic enrichment or hyperpolarization methods, including dynamic nuclear polarization (DNP) and parahydrogen-induced polarization (PHIP). DNP involves the transfer of high polarization from electronic spins of radicals to surrounding nuclear spins for signal enhancements, allowing the detection of low-abundance metabolites and real-time monitoring of metabolic activities. PHIP enables the transfer of nuclear spin polarization from parahydrogen to other nuclei for signal enhancements, particularly in proton NMR, and has been applied in studies of enzymatic reactions and cell signaling. This review provides an overview of in-cell NMR, in vivo MRS, and hyperpolarization techniques, highlighting their applications in metabolic studies and discussing challenges and future perspectives.

High-throughput omics technologies in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic, relapsing intestinal disease. Elucidation of the pathogenic mechanisms of IBD requires high-throughput technologies (HTTs) to effectively obtain and analyze large amounts of data. Recently, HTTs have been widely used in IBD, including genomics, transcriptomics, proteomics, microbiomics, metabolomics and single-cell sequencing. When combined with endoscopy, the application of these technologies can provide an in-depth understanding on the alterations of intestinal microbe diversity and abundance, the abnormalities of signaling pathway-mediated immune responses and functionality, and the evaluation of therapeutic effects, improving the accuracy of early diagnosis and treatment of IBD. This review comprehensively summarizes the development and advancement of HTTs, and also highlights the challenges and future directions of these technologies in IBD research. Although HTTs have made striking breakthrough in IBD, more standardized methods and large-scale dataset processing are still needed to achieve the goal of personalized medicine.

Recent advances on erythorbyl fatty acid esters as multi-functional food emulsifiers

Over the past few decades, food scientists have investigated a wide range of emulsifiers to manufacture stable and safe emulsion-based food products. More recently, the development of emulsifiers with multi-functionality, which is the ability to have more than two functions, has been considered as a promising strategy for resolving rancidification and microbial contamination in emulsions. Erythorbyl fatty acid esters (EFEs) synthesized by enzymatic esterification of hydrophilic erythorbic acid and hydrophobic fatty acid have been proposed as multi-functional emulsifiers since they simultaneously exhibit amphiphilic, antioxidative, and antibacterial properties in both aqueous and emulsion systems. This review provides current knowledge about EFEs in terms of enzymatic synthesis and multi-functionality. All processes for synthesizing and identifying EFEs are discussed. Each functionality of EFEs and the proposed mechanism are described with analytical methodologies and experimental details. It would provide valuable insights into the development and application of a multi-functional emulsifier in food emulsion chemistry.

Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies

INTRODUCTION

Nuclear Magnetic Resonance (NMR) spectroscopy stands as a preeminent analytical tool in the field of metabolomics. Nevertheless, when it comes to identifying metabolites present in scant amounts within various types of complex mixtures such as plants, honey, milk, and biological fluids and tissues, NMR-based metabolomics presents a formidable challenge. This predicament arises primarily from the fact that the signals emanating from metabolites existing in low concentrations tend to be overshadowed by the signals of highly concentrated metabolites within NMR spectra.

OBJECTIVES

The aim of this study is to tackle the issue of intense sugar signals overshadowing the desired metabolite signals, an optimal pulse sequence with band-selective excitation has been proposed for the suppression of sugar's moiety signals (SSMS). This sequence serves the crucial purpose of suppressing unwanted signals, with a particular emphasis on mitigating the interference caused by sugar moieties' signals.

METHODS

We have implemented this comprehensive approach to various NMR techniques, including 1D 1H presaturation (presat), 2D J-resolved (RES), 2D 1H-1H Total Correlation Spectroscopy (TOCSY), and 2D 1H-13C Heteronuclear Single Quantum Coherence (HSQC) for the samples of dates-flesh, honey, a standard stock solution of glucose, and nine amino acids, and commercial fetal bovine serum (FBS).

RESULTS

The outcomes of this approach were significant. The suppression of the high-intensity sugar signals has considerably enhanced the visibility and sensitivity of the signals emanating from the desired metabolites.

CONCLUSION

This, in turn, enables the identification of a greater number of metabolites. Additionally, it streamlines the experimental process, reducing the time required for the comparative quantification of metabolites in statistical studies in the field of metabolomics.

Synthesis of Metabolites and Metabolite-like Compounds Using Biocatalytic Systems

Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions.

Opening the Random Forest Black Box of 1H NMR Metabolomics Data by the Exploitation of Surrogate Variables

The untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.

A Sparse Model-Inspired Deep Thresholding Network for Exponential Signal Reconstruction-Application in Fast Biological Spectroscopy

The nonuniform sampling (NUS) is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partially sampled exponentials is highly expected in general signal processing and many applications. Deep learning (DL) has shown astonishing potential in this field, but many existing problems, such as lack of robustness and explainability, greatly limit its applications. In this work, by combining the merits of the sparse model-based optimization method and data-driven DL, we propose a DL architecture for spectra reconstruction from undersampled data, called MoDern. It follows the iterative reconstruction in solving a sparse model to build the neural network, and we elaborately design a learnable soft-thresholding to adaptively eliminate the spectrum artifacts introduced by undersampling. Extensive results on both synthetic and biological data show that MoDern enables more robust, high-fidelity, and ultrafast reconstruction than the state-of-the-art methods. Remarkably, MoDern has a small number of network parameters and is trained on solely synthetic data while generalizing well to biological data in various scenarios. Furthermore, we extend it to an open-access and easy-to-use cloud computing platform (XCloud-MoDern), contributing a promising strategy for further development of biological applications.

Rapid probing of glucose influx into cancer cell metabolism: using adjuvant and a pH-dependent collection of central metabolites to improve in-cell D-DNP NMR

Changes to metabolism are a hallmark of many diseases. Disease metabolism under physiological conditions can be probed in real time with in-cell NMR assays. Here, we pursued a systematic approach towards improved in-cell NMR assays. Unambiguous identifications of metabolites and of intracellular pH are afforded by a comprehensive, downloadable collection of spectral data for central carbon metabolites in the physiological pH range (4.0-8.0). Chemical shifts of glycolytic intermediates provide unique pH dependent patterns akin to a barcode. Using hyperpolarized 13C1 enriched glucose as the probe molecule of central metabolism in cancer, we find that early glycolytic intermediates are detectable in PC-3 prostate cancer cell lines, concurrently yielding intracellular pH. Using non-enriched and non-enhanced pyruvate as an adjuvant, reactions of the pentose phosphate pathway become additionally detectable, without significant changes to the barriers in upper glycolysis and to intracellular pH. The scope of tracers for in-cell observations can thus be improved by the presence of adjuvants, showing that a recently proposed effect of pyruvate in the tumor environment is paralleled by a rerouting of cancer cell metabolism towards producing building blocks for proliferation. Overall, the combined use of reference data for compound identification, site specific labelling for reducing overlap, and use of adjuvant afford increasingly detailed insight into disease metabolism.

Conversion of Similar Xenochemicals to Dissimilar Products: Exploiting Competing Reactions in Whole-Cell Catalysis

Many enzymes have latent activities that can be used in the conversion of non-natural reactants for novel organic conversions. A classic example is the conversion of benzaldehyde to a phenylacetyl carbinol, a precursor for ephedrine manufacture. It is often tacitly assumed that purified enzymes are more promising catalysts than whole cells, despite the lower cost and easier maintenance of the latter. Competing substrates inside the cell have been known to elicit currently hard-to-predict selectivities that are not easily measured inside the living cell. We employ NMR spectroscopic assays to rationally combine isomers for selective reactions in commercial S. cerevisiae. This approach uses internal competition between alternative pathways of aldehyde clearance in yeast, leading to altered selectivities compared to catalysis with the purified enzyme. In this manner, 4-fluorobenzyl alcohol and 2-fluorophenylacetyl carbinol can be formed with selectivities in the order of 90%. Modification of the cellular redox state can be used to tune product composition further. Hyperpolarized NMR shows that the cellular reaction and pathway usage are affected by the xenochemical. Overall, we find that the rational construction of ternary or more complex substrate mixtures can be used for in-cell NMR spectroscopy to optimize the upgrading of similar xenochemicals to dissimilar products with cheap whole-cell catalysts.

A Toolbox for Glutamine Use in Dissolution Dynamic Nuclear Polarization: from Enzymatic Reaction Monitoring to the Study of Cellular Metabolic Pathways and Imaging

Glutamine is under scrutiny regarding its metabolic deregulation linked to energetic reprogramming in cancer cells. Many analytical techniques have been used to better understand the impact of the metabolism of amino acids on biological processes, however only a few are suited to work with complex samples. Here, we report the use of a general dissolution dynamic nuclear polarization (D-DNP) formulation using an unexpensive radical as a multipurpose tool to study glutamine, with insights from enzymatic modelling to complex metabolic networks and fast imaging. First, hyperpolarized [5-13 C] glutamine is used as molecular probe to study the kinetic action of two enzymes: L-asparaginase that has been used as an anti-metabolic treatment for cancer, and glutaminase. These results are also compared with those acquired with another hyperpolarized amino acid, [1,4-13 C] asparagine. Second, we explored the use of hyperpolarized (HP) substrates to probe metabolic pathways by monitoring metabolic profiles arising from hyperpolarized glutamine in E. coli extracts. Finally, a highly concentrated sample formulation is proposed for the purpose of fast imaging applications. We think that this approach can be extended to formulate other amino acids as well as other metabolites and provide complementary insights into the analysis of metabolic networks.

Screening, characterization, and determination of suspected additives bimatoprost and latanoprost in cosmetics using NMR and LC-MS methods

Recently, many new types of cosmetic illegal additives have been screened in the market. Most of the new additives were new drugs or analogues with very similar structures to other prohibited additives, which were difficult to be identified by liquid chromatography-mass spectrometry (LC-MS) only. Therefore, a new strategy is proposed, which is chromatographic separation combined with nuclear magnetic resonance spectroscopy (NMR) structural identification. The suspected samples were screened by ultra-high-performance liquid chromatography tandem high-resolution mass spectrometry (UPLC-Q-TOF-MS), followed by purification and extraction through silica-gel column chromatography and preparative high-performance liquid chromatography (HPLC). Finally, the extracts were identified unambiguously by NMR as bimatoprost and latanoprost, which were identified to be new cosmetic illegal additives in eyelash serums in China. Meanwhile, bimatoprost and latanoprost were quantified by high-performance liquid chromatography tandem triple quadrupole mass spectrum (HPLC-QQQ-MS/MS). The quantitative method demonstrated good linearity in the range of approximately 0.25-50 ng/mL (R² > 0.9992), with limit of detection (LOD) and limit of quantification (LOQ) values of 0.01 and 0.03 mg/kg, respectively. The accuracy, precision, and reproducibility were confirmed to be acceptable.

Effect of culture conditions at lab-scale on metabolite composition and antibacterial and antibiofilm activities of Dunaliella tertiolecta

Dunaliella tertiolecta RCC6 was cultivated indoors in glass bubble column photobioreactors operated under batch and semi-continuous regimens and using two different conditions of light and temperature. Biomass was harvested by centrifugation, frozen, and then lyophilized. The soluble material was obtained by sequential extraction of the lyophilized biomass with solvents with a gradient of polarity (hexane, ethyl acetate, and methanol) and its metabolic composition was investigated through nuclear magnetic resonance (NMR) spectroscopy. The effect of light on chlorophyll biosynthesis was clearly shown through the relative intensities of the ¹ H NMR signals due to pheophytins. The highest signal intensity was observed for the biomasses obtained at lower light intensity, resulting in a lower light availability per cell. Under high temperature and light conditions, the ¹ H NMR spectra of the hexane extracts showed an incipient accumulation of triacylglycerols. In these conditions and under semi-continuous regimen, an enhancement of β-carotene and sterols production was observed. The antibacterial and antibiofilm activities of the extracts were also tested. Antibacterial activity was not detected, regardless of culture conditions. In contrast, the minimal biofilm inhibitory concentrations (MBICs) against Escherichia coli for the hexane extract obtained under semi-continuous regimen using high temperature and irradiance conditions was promising.

Bayesian kinetic modeling for tracer-based metabolomic data

BACKGROUND

Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups.

RESULTS

We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux .

CONCLUSIONS

Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

Curcumin-Loaded PnBA-b-POEGA Nanoformulations: A Study of Drug-Polymer Interactions and Release Behavior

The current study focuses on the development of innovative and highly-stable curcumin (CUR)-based therapeutics by encapsulating CUR in biocompatible poly(n-butyl acrylate)-block-poly(oligo(ethylene glycol) methyl ether acrylate) (PnBA-b-POEGA) micelles. State-of-the-art methods were used to investigate the encapsulation of CUR in PnBA-b-POEGA micelles and the potential of ultrasound to enhance the release of encapsulated CUR. Dynamic light scattering (DLS), attenuated total reflection Fourier transform infrared (ATR-FTIR), and ultraviolet-visible (UV-Vis) spectroscopies confirmed the successful encapsulation of CUR within the hydrophobic domains of the copolymers, resulting in the formation of distinct and robust drug/polymer nanostructures. The exceptional stability of the CUR-loaded PnBA-b-POEGA nanocarriers over a period of 210 days was also demonstrated by proton nuclear magnetic resonance (1H-NMR) spectroscopy studies. A comprehensive 2D NMR characterization of the CUR-loaded nanocarriers authenticated the presence of CUR within the micelles, and unveiled the intricate nature of the drug-polymer intermolecular interactions. The UV-Vis results also indicated high encapsulation efficiency values for the CUR-loaded nanocarriers and revealed a significant influence of ultrasound on the release profile of CUR. The present research provides new understanding of the encapsulation and release mechanisms of CUR within biocompatible diblock copolymers and has significant implications for the advancement of safe and effective CUR-based therapeutics.

Metabolomics: A New Era in the Diagnosis or Prognosis of B-Cell Non-Hodgkin's Lymphoma

A wide range of histological as well as clinical properties are exhibited by B-cell non-Hodgkin's lymphomas. These properties could make the diagnostics process complicated. The diagnosis of lymphomas at an initial stage is essential because early remedial actions taken against destructive subtypes are commonly deliberated as successful and restorative. Therefore, better protective action is needed to improve the condition of those patients who are extensively affected by cancer when diagnosed for the first time. The development of new and efficient methods for early detection of cancer has become crucial nowadays. Biomarkers are urgently needed for diagnosing B-cell non-Hodgkin's lymphoma and assessing the severity of the disease and its prognosis. New possibilities are now open for diagnosing cancer with the help of metabolomics. The study of all the metabolites synthesised in the human body is called "metabolomics." A patient's phenotype is directly linked with metabolomics, which can help in providing some clinically beneficial biomarkers and is applied in the diagnostics of B-cell non-Hodgkin's lymphoma. In cancer research, it can analyse the cancerous metabolome to identify the metabolic biomarkers. This review provides an understanding of B-cell non-Hodgkin's lymphoma metabolism and its applications in medical diagnostics. A description of the workflow based on metabolomics is also provided, along with the benefits and drawbacks of various techniques. The use of predictive metabolic biomarkers for the diagnosis and prognosis of B-cell non-Hodgkin's lymphoma is also explored. Thus, we can say that abnormalities related to metabolic processes can occur in a vast range of B-cell non-Hodgkin's lymphomas. The metabolic biomarkers could only be discovered and identified as innovative therapeutic objects if we explored and researched them. In the near future, the innovations involving metabolomics could prove fruitful for predicting outcomes and bringing out novel remedial approaches.

Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra

The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in 1H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. We show that our model can detect signal regions effectively and minimize classification errors between different types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental 1H NMR spectra.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Cholesterol and M2 Rendezvous in Budding and Scission of Influenza A Virus

The cholesterol of the host cell plasma membrane and viral M2 protein plays a crucial role in multiple stages of infection and replication of the influenza A virus. Cholesterol is required for the formation of heterogeneous membrane microdomains (or rafts) in the budozone of the host cell that serves as assembly sites for the viral components. The raft microstructures act as scaffolds for several proteins. Cholesterol may further contribute to the mechanical forces necessary for membrane scission in the last stage of budding and help to maintain the stability of the virus envelope. The M2 protein has been shown to cause membrane scission in model systems by promoting the formation of curved lipid bilayer structures that, in turn, can lead to membrane vesicles budding off or scission intermediates. Membrane remodeling by M2 is intimately linked with cholesterol as it affects local lipid composition, fluidity, and stability of the membrane. Thus, both cholesterol and M2 protein contribute to the efficient and proper release of newly formed influenza viruses from the virus-infected cells.

Metabolomic Signatures in Doxorubicin-Induced Metabolites Characterization, Metabolic Inhibition, and Signaling Pathway Mechanisms in Colon Cancer HCT116 Cells

Doxorubicin (DOX) is a chemotherapeutic agent is used for various cancer cells. To characterize the chemical structural components and metabolic inhibition, we applied a DOX to HCT116 colon cancer cells using an independent metabolites profiling approach. Chemical metabolomics has been involved in the new drug delivery systems. Metabolomics profiling of DOX-applied HCT116 colon cancer cellular metabolisms is rare. We used 1H nuclear magnetic resonance (NMR) spectroscopy in this study to clarify how DOX exposure affected HCT116 colon cancer cells. Metabolomics profiling in HCT116 cells detects 50 metabolites. Tracking metabolites can reveal pathway activities. HCT116 colon cancer cells were evenly treated with different concentrations of DOX for 24 h. The endogenous metabolites were identified by comparison with healthy cells. We found that acetate, glucose, glutamate, glutamine, sn-glycero-3-phosphocholine, valine, methionine, and isoleucine were increased. Metabolic expression of alanine, choline, fumarate, taurine, o-phosphocholine, inosine, lysine, and phenylalanine was decreased in HCT116 cancer cells. The metabolic phenotypic expression is markedly altered during a high dose of DOX. It is the first time that there is a metabolite pool and phenotypic expression in colon cancer cells. Targeting the DOX-metabolite axis may be a novel strategy for improving the curative effect of DOX-based therapy for colon cancer cells. These methods facilitate the routine metabolomic analysis of cancer cells.

Metabolic biomarkers of radiotherapy response in plasma and tissue of an IDH1 mutant astrocytoma mouse model

Astrocytomas are the most common subtype of brain tumors and no curative treatment exist. Longitudinal assessment of patients, usually via Magnetic Resonance Imaging (MRI), is crucial since tumor progression may occur earlier than clinical progression. MRI usually provides a means for monitoring the disease, but it only informs about the structural changes of the tumor, while molecular changes can occur as a treatment response without any MRI-visible change. Radiotherapy (RT) is routinely performed following surgery as part of the standard of care in astrocytomas, that can also include chemotherapy involving temozolomide. Monitoring the response to RT is a key factor for the management of patients. Herein, we provide plasma and tissue metabolic biomarkers of treatment response in a mouse model of astrocytoma that was subjected to radiotherapy. Plasma metabolic profiles acquired over time by Liquid Chromatography Mass Spectrometry (LC/MS) were subjected to multivariate empirical Bayes time-series analysis (MEBA) and Receiver Operating Characteristic (ROC) assessment including Random Forest as the classification strategy. These analyses revealed a variation of the plasma metabolome in those mice that underwent radiotherapy compared to controls; specifically, fumarate was the best discriminatory feature. Additionally, Nuclear Magnetic Resonance (NMR)-based 13C-tracing experiments were performed at end-point utilizing [U-13C]-Glutamine to investigate its fate in the tumor and contralateral tissues. Irradiated mice displayed lower levels of glycolytic metabolites (e.g. phosphoenolpyruvate) in tumor tissue, and a higher flux of glutamine towards succinate was observed in the radiation cohort. The plasma biomarkers provided herein could be validated in the clinic, thereby improving the assessment of brain tumor patients throughout radiotherapy. Moreover, the metabolic rewiring associated to radiotherapy in tumor tissue could lead to potential metabolic imaging approaches for monitoring treatment using blood draws.

NMR-based isotope editing, chemoselection and isotopomer distribution analysis in stable isotope resolved metabolomics

NMR is a very powerful tool for identifying and quantifying compounds within complex mixtures without the need for individual standards or chromatographic separation. Stable Isotope Resolved Metabolomics (or SIRM) is an approach to following the fate of individual atoms from precursors through metabolic transformation, producing an atom-resolved metabolic fate map. However, extracts of cells or tissue give rise to very complex NMR spectra. While multidimensional NMR experiments may partially overcome the spectral overlap problem, additional tools may be needed to determine site-specific isotopomer distributions. NMR is especially powerful by virtue of its isotope editing capabilities using NMR active nuclei such as 13C, 15N, 19F and 31P to select molecules containing just these atoms in a complex mixture, and provide direct information about which atoms are present in identified compounds and their relative abundances. The isotope-editing capability of NMR can also be employed to select for those compounds that have been selectively derivatized with an NMR-active stable isotope at particular functional groups, leading to considerable spectral simplification. Here we review isotope analysis by NMR, and methods of chemoselection both for spectral simplification, and for enhanced isotopomer analysis.

In-cell NMR: Why and how?

NMR spectroscopy has been applied to cells and tissues analysis since its beginnings, as early as 1950. We have attempted to gather here in a didactic fashion the broad diversity of data and ideas that emerged from NMR investigations on living cells. Covering a large proportion of the periodic table, NMR spectroscopy permits scrutiny of a great variety of atomic nuclei in all living organisms non-invasively. It has thus provided quantitative information on cellular atoms and their chemical environment, dynamics, or interactions. We will show that NMR studies have generated valuable knowledge on a vast array of cellular molecules and events, from water, salts, metabolites, cell walls, proteins, nucleic acids, drugs and drug targets, to pH, redox equilibria and chemical reactions. The characterization of such a multitude of objects at the atomic scale has thus shaped our mental representation of cellular life at multiple levels, together with major techniques like mass-spectrometry or microscopies. NMR studies on cells has accompanied the developments of MRI and metabolomics, and various subfields have flourished, coined with appealing names: fluxomics, foodomics, MRI and MRS (i.e. imaging and localized spectroscopy of living tissues, respectively), whole-cell NMR, on-cell ligand-based NMR, systems NMR, cellular structural biology, in-cell NMR… All these have not grown separately, but rather by reinforcing each other like a braided trunk. Hence, we try here to provide an analytical account of a large ensemble of intricately linked approaches, whose integration has been and will be key to their success. We present extensive overviews, firstly on the various types of information provided by NMR in a cellular environment (the "why", oriented towards a broad readership), and secondly on the employed NMR techniques and setups (the "how", where we discuss the past, current and future methods). Each subsection is constructed as a historical anthology, showing how the intrinsic properties of NMR spectroscopy and its developments structured the accessible knowledge on cellular phenomena. Using this systematic approach, we sought i) to make this review accessible to the broadest audience and ii) to highlight some early techniques that may find renewed interest. Finally, we present a brief discussion on what may be potential and desirable developments in the context of integrative studies in biology.

A Micro-Scale Analytical Method for Determining Glycogen Turnover by NMR and FTMS

Glycogen is a readily deployed intracellular energy storage macromolecule composed of branched chains of glucose anchored to the protein glycogenin. Although glycogen primarily occurs in the liver and muscle, it is found in most tissues, and its metabolism has been shown to be important in cancers and immune cells. Robust analysis of glycogen turnover requires stable isotope tracing plus a reliable means of quantifying total and labeled glycogen derived from precursors such as 13C6-glucose. Current methods for analyzing glycogen are time- and sample-consuming, at best semi-quantitative, and unable to measure stable isotope enrichment. Here we describe a microscale method for quantifying both intact and acid-hydrolyzed glycogen by ultra-high-resolution Fourier transform mass spectrometric (UHR-FTMS) and/or NMR analysis in stable isotope resolved metabolomics (SIRM) studies. Polar metabolites, including intact glycogen and their 13C positional isotopomer distributions, are first measured in crude biological extracts by high resolution NMR, followed by rapid and efficient acid hydrolysis to glucose under N2 in a focused beam microwave reactor, with subsequent analysis by UHR-FTMS and/or NMR. We optimized the microwave digestion time, temperature, and oxygen purging in terms of recovery versus degradation and found 10 min at 110−115 °C to give >90% recovery. The method was applied to track the fate of 13C6-glucose in primary human lung BEAS-2B cells, human macrophages, murine liver and patient-derived tumor xenograft (PDTX) in vivo, and the fate of 2H7-glucose in ex vivo lung organotypic tissue cultures of a lung cancer patient. We measured the incorporation of 13C6-glucose into glycogen and its metabolic intermediates, UDP-Glucose and glucose-1-phosphate, to demonstrate the utility of the method in tracing glycogen turnover in cells and tissues. The method offers a quantitative, sensitive, and convenient means to analyze glycogen turnover in mg amounts of complex biological materials.

Follicular Fluid: A Powerful Tool for the Understanding and Diagnosis of Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) represents one of the leading causes of anovulatory infertility and affects 5% to 20% of women worldwide. Until today, both the subsequent etiology and pathophysiology of PCOS remain unclear, and patients with PCOS that undergo assisted reproductive techniques (ART) might present a poor to exaggerated response, low oocyte quality, ovarian hyperstimulation syndrome, as well as changes in the follicular fluid metabolites pattern. These abnormalities originate a decrease of Metaphase II (MII) oocytes and decreased rates for fertilization, cleavage, implantation, blastocyst conversion, poor egg to follicle ratio, and increased miscarriages. Focus on obtaining high-quality embryos has been taken into more consideration over the years. Nowadays, the use of metabolomic analysis in the quantification of proteins and peptides in biological matrices might predict, with more accuracy, the success in assisted reproductive technology. In this article, we review the use of human follicular fluid as the matrix in metabolomic analysis for diagnostic and ART predictor of success for PCOS patients.

1H NMR Profiling of Honey Bee Bodies Revealed Metabolic Differences between Summer and Winter Bees

Simple Summary

The European honey bee, Apis mellifera, is well-known to have two distinct populations in temperate climate zone: short-living summer bees and long-living winter bees. Several biological factors related to the different lifespans of the two populations have been studied. However, the link between the metabolic changes and basic physiological features in the bodies of summer bees and winter bees is limited. This study aimed to identify the metabolic fingerprints that characterize summer and winter bees using proton nuclear magnetic resonance (¹H NMR) spectroscopy. In total, we found 28 significantly changed metabolites between the two populations. The results suggest that the metabolites detected in honey bee bodies can distinguish the summer and winter bees. Changes in carbohydrates, amino acids, choline-containing compounds, and an unknown compound were noticeable during the transition from summer bees to winter bees. The results from this study give us a broad perspective on honey bee metabolism that will support future research related to honey bee lifespan and overwintering management.

Abstract

In temperate climates, honey bee workers of the species Apis mellifera have different lifespans depending on the seasonal phenotype: summer bees (short lifespan) and winter bees (long lifespan). Many studies have revealed the biochemical parameters involved in the lifespan differentiation of summer and winter bees. However, comprehensive information regarding the metabolic changes occurring in their bodies between the two is limited. This study used proton nuclear magnetic resonance (¹H NMR) spectroscopy to analyze the metabolic differences between summer and winter bees of the same age. The multivariate analysis showed that summer and winter bees could be distinguished based on their metabolic profiles. Among the 36 metabolites found, 28 metabolites have displayed significant changes from summer to winter bees. Compared to summer bees, trehalose in winter bees showed 1.9 times higher concentration, and all amino acids except for proline and alanine showed decreased patterns. We have also detected an unknown compound, with a CH₃ singlet at 2.83 ppm, which is a potential biomarker that is about 13 times higher in summer bees. Our results show that the metabolites in summer and winter bees have distinctive characteristics; this information could provide new insights and support further studies on honey bee longevity and overwintering.

Potential of nuclear magnetic resonance metabolomics in the study of prostate cancer

Nuclear magnetic resonance (NMR) metabolomics is a powerful analytical technique and a tool which has unique characteristics and capabilities for the evaluation of a number of biochemicals/metabolites of cancer and other disease processes that are present in biofluids (urine and blood) and tissues. The potential of NMR metabolomics in prostate cancer (PCa) has been explored by researchers and its usefulness has been documented. A large number of metabolites such as citrate, choline, and sarcosine were detected by NMR metabolomics from biofluids and tissues related to PCa and their levels were compared with controls and benign prostatic hyperplasia. The changes in the levels of these metabolites aid in the diagnosis and help to understand the dysregulated metabolic pathways in PCa. We review recent studies on in vitro and ex vivo NMR spectroscopy-based PCa metabolomics and its possible role as a diagnostic tool.

Application of the Metabolomics Approach in Food Authentication

The authentication of food products is essential for food quality and safety. Authenticity assessments are important to ensure that the ingredients or contents of food products are legitimate and safe to consume. The metabolomics approach is an essential technique that can be utilized for authentication purposes. This study aimed to summarize food authentication through the metabolomics approach, to study the existing analytical methods, instruments, and statistical methods applied in food authentication, and to review some selected food commodities authenticated using metabolomics-based methods. Various databases, including Google Scholar, PubMed, Scopus, etc., were used to obtain previous research works relevant to the objectives. The review highlights the role of the metabolomics approach in food authenticity. The approach is technically implemented to ensure consumer protection through the strict inspection and enforcement of food labeling. Studies have shown that the study of metabolomics can ultimately detect adulterant(s) or ingredients that are added deliberately, thus compromising the authenticity or quality of food products. Overall, this review will provide information on the usefulness of metabolomics and the techniques associated with it in successful food authentication processes, which is currently a gap in research that can be further explored and improved.

Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine ¹H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.

NMR Analysis of Carboxylate Isotopomers of 13C-Metabolites by Chemoselective Derivatization with 15N-Cholamine

A substantial fraction of common metabolites contains carboxyl functional groups. Their ¹³C isotopomer analysis by nuclear magnetic resonance (NMR) is hampered by the low sensitivity of the ¹³C nucleus, the slow longitudinal relaxation for the lack of an attached proton, and the relatively low chemical shift dispersion of carboxylates. Chemoselective (CS) derivatization is a means of tagging compounds in a complex mixture via a specific functional group. ¹⁵N₁-cholamine has been shown to be a useful CS agent for carboxylates, producing a peptide bond that can be detected via ¹⁵N-attached H with high sensitivity in heteronuclear single quantum coherence experiments. Here, we report an improved method of derivatization and show how ¹³C-enrichment at the carboxylate and/or the adjacent carbon can be determined via one- and two-bond coupling of the carbons adjacent to the cholamine ¹⁵N atom in the derivatives. We have applied this method for the determination of ¹³C isotopomer distribution in the extracts of A549 cell culture and liver tissue from a patient-derived xenograft mouse.

Lipids: biomarkers of healthy aging

Life expectancy, and longevity have been increasing in recent years. However, this is, in most cases, accompanied by age-related diseases. Thus, it became essential to better understand the mechanisms inherent to aging, and to establish biomarkers that characterize this physiological process. Among all biomolecules, lipids appear to be a good target for the study of these biomarkers. In fact, some lipids have already been associated with age-related diseases. With the development of analytical techniques such as Mass Spectrometry, and Nuclear Magnetic Resonance, Lipidomics has been increasingly used to study pathological, and physiological states of an organism. Thus, the study of serum, and plasma lipidome in centenarians, and elderly individuals without age-related diseases can be a useful tool for the identification of aging biomarkers, and to understand physiological aging, and longevity. This review focus on the importance of lipids as biomarkers of aging, and summarize the changes in the lipidome that have been associated with aging, and longevity.

NMR-Based Metabolomics in Cancer Research

Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.

Tailoring the nuclear Overhauser effect for the study of small and medium-sized molecules by solvent viscosity manipulation

The nuclear Overhauser effect (NOE) is a consequence of cross-relaxation between nuclear spins mediated by dipolar coupling. Its sensitivity to internuclear distances has made it an increasingly important tool for the determination of through-space atom proximity relationships within molecules of sizes ranging from the smallest systems to large biopolymers. With the support of sophisticated FT-NMR techniques, the NOE plays an essential role in structure elucidation, conformational and dynamic investigations in liquid-state NMR. The efficiency of magnetization transfer by the NOE depends on the molecular rotational correlation time, whose value depends on solution viscosity. The magnitude of the NOE between ¹H nuclei varies from +50% when molecular tumbling is fast to -100% when it is slow, the latter case corresponding to the spin diffusion limit. In an intermediate tumbling regime, the NOE may be vanishingly small. Increasing the viscosity of the solution increases the motional correlation time, and as a result, otherwise unobservable NOEs may be revealed and brought close to the spin diffusion limit. The goal of this review is to report the resolution of structural problems that benefited from the manipulation of the negative NOE by means of viscous solvents, including examples of molecular structure determination, conformation elucidation and mixture analysis (the ViscY method).

Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics

Prostate cancer (PCa) is the second most diagnosed cancer in men worldwide. For its screening, serum prostate specific antigen (PSA) test has been largely performed over the past decade, despite its lack of accuracy and inability to distinguish indolent from aggressive disease. Metabolomics has been widely applied in cancer biomarker discovery due to the well-known metabolic reprogramming characteristic of cancer cells. Most of the metabolomic studies have reported alterations in urine of PCa patients due its noninvasive collection, but the analysis of prostate tissue metabolome is an ideal approach to disclose specific modifications in PCa development. This review aims to summarize and discuss the most recent findings from tissue and urine metabolomic studies applied to PCa biomarker discovery. Eighteen metabolites were found consistently altered in PCa tissue among different studies, including alanine, arginine, uracil, glutamate, fumarate, and citrate. Urine metabolomic studies also showed consistency in the dysregulation of 15 metabolites and, interestingly, alterations in the levels of valine, taurine, leucine and citrate were found in common between urine and tissue studies. These findings unveil that the impact of PCa development in human metabolome may offer a promising strategy to find novel biomarkers for PCa diagnosis.

Review and prospect: NMR spectroscopy denoising and reconstruction with low-rank Hankel matrices and tensors

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical tool in chemistry, biology, and life science, but it suffers from relatively low sensitivity and long acquisition time. Thus, improving the apparent signal-to-noise ratio and accelerating data acquisition became indispensable. In this review, we summarize the recent progress on low-rank Hankel matrix and tensor methods, which exploit the exponential property of free-induction decay signals, to enable effective denoising and spectra reconstruction. We also outline future developments that are likely to make NMR spectroscopy a far more powerful technique.

Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry-Based Metabolomics

Compared to one-dimensional gas chromatography with mass spectrometry (GC-MS), GC × GC-MS provides significantly increased peak capacity, resolution, and sensitivity for analysis of complex biological samples. In the last decade, GC × GC-MS has been increasingly applied to the discovery of metabolite biomarkers and elucidation of metabolic mechanisms in human diseases. The recent development of coupling GC × GC with a high-resolution mass spectrometer further accelerates these metabolomic applications. In this chapter, we will briefly review the instrumentation, sample preparation, data analysis, and applications of GC × GC-MS-based metabolomic analysis.

Nuclear Magnetic Resonance-Based Metabolomic Analysis of the Anticancer Effect of Metformin Treatment on Cholangiocarcinoma Cells

Metformin is a widely prescribed anti-diabetes drug with potential utilities for cancer therapies. Several studies have related metformin to the reduced risk of cholangiocarcinoma (CCA), highlighting its potentialities for the treatments of CCA. However, the underlying molecular mechanisms remain elusive. Here, we demonstrated that metformin treatment could inhibit proliferations of the human CCA cell lines Mz-ChA-1 and QBC939 in dose-dependent manners. The NMR-based metabonomic analyses showed distinct discriminations between the metformin-treated (Met) and control (Ctrl) groups of both CCA cells. Characteristic metabolites were identified by a combination of multivariate statistical analysis of 1D ¹H-NMR spectral data and the pair-wise t-test of metabolite levels. We then identified four significantly altered metabolic pathways based on the characteristic metabolites, including glucose metabolism, oxidative stress-related metabolism, energy metabolism, and amino acids metabolism. Comparing CCA cells with normal human umbilical vein endothelial cells (HUVECs), we found that metformin treatment profoundly promoted glycolysis and specifically increased the levels of BCAAs and UDP-GlcNAc, implying the occurrence of autophagy and cell cycle arrest in metformin-treated CAA cells. This work provides a mechanistic understanding of the anticancer effect of metformin treatment on CAA cells, and is beneficial to further developments of metformin as an anticancer drug.

The utility of nuclear magnetic resonance spectroscopy in assisted reproduction

Infertility affects approximately 15-20% of individuals of reproductive age worldwide. Over the last 40 years, assisted reproductive technology (ART) has helped millions of childless couples. However, ART is limited by a low success rate and risk of multiple gestations. Devising methods for selecting the best gamete or embryo that increases the ART success rate and prevention of multiple gestation has become one of the key goals in ART today. Special emphasis has been placed on the development of non-invasive approaches, which do not require perturbing the embryonic cells, as the current morphology-based embryo selection approach has shortcomings in predicting the implantation potential of embryos. An observed association between embryo metabolism and viability has prompted researchers to develop metabolomics-based biomarkers. Nuclear magnetic resonance (NMR) spectroscopy provides a non-invasive approach for the metabolic profiling of tissues, gametes and embryos, with the key advantage of having a minimal sample preparation procedure. Using NMR spectroscopy, biologically important molecules can be identified and quantified in intact cells, extracts or secretomes. This, in turn, helps to map out the active metabolic pathways in a system. The present review covers the contribution of NMR spectroscopy in assisted reproduction at various stages of the process.

Self-Stimulated Pulse Echo Trains from Inhomogeneously Broadened Spin Ensembles

We show experimentally and describe theoretically how a conventional magnetic resonance Hahn echo sequence can lead to a self-stimulated pulse echo train when an inhomogeneously broadened spin ensemble is coupled to a resonator. Effective strong coupling between the subsystems assures that the first Hahn echo can act as a refocusing pulse on the spins, leading to self-stimulated secondary echoes. Within the framework of mean field theory, we show that this process can continue multiple times leading to a train of echoes. We introduce an analytical model that explains the shape of the first echo and numerical results that account well for the experimentally observed shape and strength of the echo train and provides insights into the collective effects involved.

Factors Governing the Chemical Stability and NMR Parameters of Uracil Tautomers and Its 5-Halogen Derivatives

We report on the density functional theory (DFT) modelling of structural, energetic and NMR parameters of uracil and its derivatives (5-halogenouracil (5XU), X = F, Cl, Br and I) in vacuum and in water using the polarizable continuum model (PCM) and the solvent model density (SMD) approach. On the basis of the obtained results, we conclude that the intramolecular electrostatic interactions are the main factors governing the stability of the six tautomeric forms of uracil and 5XU. Two indices of aromaticity, the harmonic oscillator model of aromaticity (HOMA), satisfying the geometric criterion, and the nuclear independent chemical shift (NICS), were applied to evaluate the aromaticity of uracil and its derivatives in the gas phase and water. The values of these parameters showed that the most stable tautomer is the least aromatic. A good performance of newly designed xOPBE density functional in combination with both large aug-cc-pVQZ and small STO(1M)-3G basis sets for predicting chemical shifts of uracil and 5-fluorouracil in vacuum and water was observed. As a practical alternative for calculating the chemical shifts of challenging heterocyclic compounds, we also propose B3LYP calculations with small STO(1M)-3G basis set. The indirect spin-spin coupling constants predicted by B3LYP/aug-cc-pVQZ(mixed) method reproduce the experimental data for uracil and 5-fluorouracil well.

Effect of daunorubicin on acute promyelocytic leukemia cells using nuclear magnetic resonance spectroscopy-based metabolomics

The aim of this study was to determine several key metabolites as potential biomarkers of daunorubicin (DNR) treatment of acute promyelocytic leukemia (APL) using APL blasts and NB4 cells. Samples which were obtained from 16 newly diagnosed APL patients and human APL NB4 cell lines were exposed to increasing concentrations of DNR (0 μM, 0.1 μM, 0.5 μM and 1.0 μM). Electron microscopy and Nuclear Magnetic Resonance (NMR) spectroscopy confirmed that there were clear differences between controls and DNR-treated groups, with the resultant models having excellent predictive and discriminative abilities. Four metabolites meeting the biomarker requirements were identified. KEGG analyses revealed that these biomarkers were associated with the metabolism of fat, choline, and glucose. These findings offered vital information about the effects of chemotherapies on the whole body biochemistry which might be important for monitoring apoptosis and injury to cells in order to reduce chemotherapies-induced side effects.