Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

Metabolomics-Based Mechanistic Insights into Revealing the Adverse Effects of Pesticides on Plants: An Interactive Review

In plant biology, metabolomics is often used to quantitatively assess small molecules, metabolites, and their intermediates in plants. Metabolomics has frequently been applied to detect metabolic alterations in plants exposed to various biotic and abiotic stresses, including pesticides. The widespread use of pesticides and agrochemicals in intensive crop production systems is a serious threat to the functionality and sustainability of agroecosystems. Pesticide accumulation in soil may disrupt soil-plant relationships, thereby posing a pollution risk to agricultural output. Application of metabolomic techniques in the assessment of the biological consequences of pesticides at the molecular level has emerged as a crucial technique in exposome investigations. State-of-the-art metabolomic approaches such as GC-MS, LC-MS/MS UHPLC, UPLC-IMS-QToF, GC/EI/MS, MALDI-TOF MS, and ¹H-HR-MAS NMR, etc., investigating the harmful effects of agricultural pesticides have been reviewed. This updated review seeks to outline the key uses of metabolomics related to the evaluation of the toxicological impacts of pesticides on agronomically important crops in exposome assays as well as bench-scale studies. Overall, this review describes the potential uses of metabolomics as a method for evaluating the safety of agricultural chemicals for regulatory applications. Additionally, the most recent developments in metabolomic tools applied to pesticide toxicology and also the difficulties in utilizing this approach are discussed.

Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis

Endometriosis has a wide range of clinical manifestations, and the disease course is unpredictable, making the diagnosis a challenging task. Despite significant advances in the pathophysiology of endometriosis and various proposed theories, the exact etiology is not fully understood and is still unknown. The most commonly used biomarker of endometriosis is CA-125, however, it is nonspecific and is applied for cancers diagnosis. Therefore, the development of reliable noninvasive diagnostic tests for the early diagnosis of endometriosis remains one of the top priorities. Omics technologies are very promising approaches for constructing diagnostic models and biomarker discovery. Their use can greatly facilitate the study of such a complex disease as endometriosis. Nowadays, powerful analytical platforms commonly used in omics, such as gas and liquid chromatography with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, have proven to be a promising tools for biomarker discovery. The aim of this review is to summarize the various features of the analytical approaches, practical challenges and features of gas and liquid chromatography with MS and NMR spectroscopy (including sample processing protocols, technological advancements, and methodology) used for profiling of metabolites, lipids, peptides and proteins in physiological fluids and tissues from patients with endometriosis. In addition, this report devotes special attention to the issue of how comprehensive analyses of these profiles can effectively contribute to the study of endometriosis. The search query included reports published between 2012 and 2022 years in PubMed, Web-of-Science, SCOPUS, Science Direct.

Visualizing how inclusion of higher reciprocal space in SWAXS data analysis improves shape restoration of biomolecules: case of lysozyme

Query remains whether use of increased resolution data from X-ray scattering aids in better understanding of the dynamic shape of the biomolecule in solution? To address this, we acquired Small/Wide angle X-ray scattering (SWAXS) data in the q range of 0.008 - 1.72 Å^-1 from dilute solutions of lysozyme (0.9 to 5 mg/ml). Samples lacked any interparticulate effect and datasets showed Bragg peaks at q∼0.325, 0.65 and 1.4 Å^-1, as reported before by other authors. Considering an averaged profile, we estimated shape parameters and distance distribution profiles of interatomic vectors by gradually increasing input q_max value. Interestingly, use of higher resolution led to emergence of new peaks amongst smaller vectors. Deconvolution of these peaks provided positions of smaller peaks which correlated well with an earlier theoretical work. These peaks arise from secondary structures or due to non-uniform internal motions within the larger shape of this protein. Dummy residue modeling considering uniform density yielded model(s) with holes or cavities when considering higher q values implying limitations of this method. Employing normal mode calculations, we searched for better fitting model of lysozyme using differentially ranged SWAXS data and a crystal structure of lysozyme as starting structure. Comparison of refined models with structures from crystallography and NMR data showed that use of data till mid q region resulted in adjustments near the center of mass of starting structure, and inclusion of higher resolution induced pan-structure adjustments. We conclude that high resolution SWAXS data analysis provides additional dimension towards understanding biomolecular structural dynamics.Communicated by Ramaswamy H. Sarma.

Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics

Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Quantification of polyols in sugar-free foodstuffs by qNMR

We present a qNMR method for the determination of low calories sweeteners (erythritol, mannitol, maltitol, sorbitol, isomalt and xylitol) in sugar-free foodstuff. The structural similarities of these compounds determine often a severe spectral overlap that hampers their quantification via conventional 1D and 2D NMR spectra. This problem is here overcome by exploiting the resolving capabilities of the CSSF-TOCSY experiment, allowing the quantification of all six polyols, with satisfactory results in terms of LoQ (2.8-7.4 mg/L for xylitol, mannitol, sorbitol, 15 mg/L for erythritol, 38 mg/L for maltitol and 91 mg/L for isomalt), precision (RSD% 0.40-4.03), trueness (bias% 0.15-4.81), and recovery (98-104%). Polyol's quantification in different sugar-free confectionary products was performed after a simple water extraction without any additional sample treatment. While these results demonstrate the robustness of the proposed method for polyols quantification in low calories foods, its applicability can be further extended to other food matrices or biofluids.

Triticonazole enantiomers induced enantioselective metabolic phenotypes in Fusarium graminearum and HepG2 cells

The management of Fusarium head blight relies heavily on triazole fungicides. Most of triazole fungicides are chiral, and their enantioselective effects on metabolic phenotypes are poorly understood. Herein, we analyzed the bioactivity of triticonazole against Fusarium graminearum, and ¹H-nuclear magnetic resonance-based metabolomics was used to assess the metabolic disturbances of triticonazole enantiomers in Fusarium graminearum and human hepatocarcinoma cells. Results indicated that the bioactivity of R-triticonazole was 4.28-fold higher than its antipode since it bound stronger with fungal CYP51B and induced more abnormal metabolic processes of Fusarium graminearum, including lipid metabolism, glycolysis, and amino acid metabolism. In human hepatocarcinoma cells, pathways of "alanine, aspartic acid and glutamate metabolism" and "pyruvate metabolism" were disturbed significantly by R-triticonazole; "phenylalanine metabolism" and "taurine-hypotaurine metabolism" were abnormal in the exposure of S-triticonazole. These results suggested that R- and S-triticonazole could affect different metabolic pathways of human hepatocarcinoma cells, and the massively use of inefficient S-triticonazole should be avoided. Our data will help to better understand the enantioselectivity of chiral pesticides and provide a reference for the development of green pesticides.

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.

Enzyme kinetics by real-time quantitative NMR (qNMR) spectroscopy with progress curve analysis

This review article summarizes how the experimental data obtained using quantitative nuclear magnetic resonance (qNMR) spectroscopy can be combined with progress curve analysis to determine enzyme kinetic parameters. The qNMR approach enables following the enzymatic conversion of the substrate to the product in real-time by a continuous collection of spectra. The Lambert-W function, a closed-form solution to the time-dependent substrate/product kinetics of the rate equation, can estimate the Michaelis-Menten constant (K_M.) and the maximum velocity (V_max) from a single experiment. This article highlights how the qNMR data is well suited for analysis using the Lambert-W function with three different applications. Results from studies on acetylcholinesterase (acetylcholine to acetic acid and choline), β-Galactosidase (lactose to glucose and galactose), and invertase (sucrose to glucose and fructose) are presented. Furthermore, an additional example of how the progress curve analysis is applied to understand the inhibitory role of the artificial sweetener sucralose on sucrose's enzymatic conversion by invertase is discussed. With the wide availability of NMR spectrometers in academia and industries, including bench-top systems with permanent magnets, and the potential to enhance sensitivity using dynamic nuclear polarization in combination with ultrafast methods, the NMR-based enzyme kinetics could be considered a valuable tool for broader applications in the field of enzyme kinetics.

NMR-Based Metabolomic Analysis of Cardiac Tissues Clarifies Molecular Mechanisms of CVB3-Induced Viral Myocarditis and Dilated Cardiomyopathy

Viral myocarditis (VMC), which is defined as inflammation of the myocardium with consequent myocardial injury, may develop chronic disease eventually leading to dilated cardiomyopathy (DCM). Molecular mechanisms underlying the progression from acute VMC (aVMC), to chronic VMC (cVMC) and finally to DCM, are still unclear. Here, we established mouse models of VMC and DCM with Coxsackievirus B3 infection and conducted NMR-based metabolomic analysis of aqueous metabolites extracted from cardiac tissues of three histologically classified groups including aVMC, cVMC and DCM. We showed that these three pathological groups were metabolically distinct from their normal counterparts and identified three impaired metabolic pathways shared by these pathological groups relative to normal controls, including nicotinate and nicotinamide metabolism; alanine, aspartate and glutamate metabolism; and D-glutamine and D-glutamate metabolism. We also identified two extra impaired metabolic pathways in the aVMC group, including glycine, serine and threonine metabolism; and taurine and hypotaurine metabolism Furthermore, we identified potential cardiac biomarkers for metabolically distinguishing these three pathological stages from normal controls. Our results indicate that the metabolomic analysis of cardiac tissues can provide valuable insights into the molecular mechanisms underlying the progression from acute VMC to DCM.

Untargeted serum metabolomics reveals specific metabolite abnormalities in patients with Crohn's disease

Crohn's disease (CD) is a subtype of inflammatory bowel disease (IBD) characterized by skip intestinal lesions that can occur in any part of the gastrointestinal tract. Currently, the diagnosis of CD is based on clinical history, physical examination and complementary diagnostic tests. It is challenging for physicians to make a definitive diagnosis. This study aimed to analyze the variation in metabolites in CD serum and identify potential predictive biomarkers of CD diagnosis. We collected serum samples from 316 subjects, including patients with CD and healthy controls (HCs). Serum metabolomics was conducted using liquid chromatography coupled to mass spectrometry. Potential biomarkers were screened and evaluated by univariate and multivariate analyses. A panel of two metabolites (deoxycholic acid and palmitic amide) was identified as a specific biomarker of CD. Receiver operating characteristic analysis (ROC) showed that the panel had a sensitivity of 80.25% with a specificity of 95.54% in discriminating CD patients from healthy controls. The biomarkers identified are increased in CD compared with healthy controls. Our approach successfully identified serum biomarkers associated with CD patients. The potential biomarkers indicated that CD metabolic disturbance might be associated with bile acid biosynthesis, fatty acids and energy metabolism.

Metabolomics study of the desiccation and recovery process in the resurrection plants Ramonda serbica and R. nathaliae

INTRODUCTION

Ramonda serbica and R. nathaliae are resurrection plants that have the remarkable ability to survive the complete desiccation of their vegetative organs (i.e. leaves, stem, roots) during periods of drought and rapidly revive when rewatered and rehydrated.

OBJECTIVE

To investigate metabolic changes in R. serbica and R. nathaliae during their desiccation and recovery process METHODS: Proton nuclear magnetic resonance (¹ H-NMR) and gas chromatography-mass spectrometry (GC-MS)-based metabolomics approach coupled with multivariate data analysis was utilised to identify the metabolomes of the plants from 90 biological replicates.

RESULTS

Sucrose and the polyphenolic glycoside myconoside were predominant in almost equal amounts in all samples studied, regardless of their water content at sampling. During the dehydration process, a decrease in the relative content of fructose, galactose, and galactinol was observed while the contents of those metabolites were preserved in the partially rehydrated plants. Raffinose and myo-inositol were accumulated in dry samples.

CONCLUSION

Using ¹ H-NMR and GC-MS as two complementary analytical platforms provided a more complete picture of the metabolite composition for investigation of the desiccation and recovery process in resurrection plants.

Metabolic regulation mechanism of Aconiti Radix Cocta extract in rats based on 1H-NMR metabonomics

Objective

To establish a metabonomics research technique based on the combination of ¹H-NMR and multivariate statistical analysis, so as to explore the metabolic regulation mechanism of Aconiti Radix Cocta extract (ARCE) in rat tissues and serum.

Methods

SD rats were randomly divided into blank group, female group and male group. The ¹H-NMR technique was used to collect the information of rat tissues and serum samples in each group. The principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and other methods were used for data pattern recognition, so as to screen out potential differential metabolites and metabolic pathways, and then network analysis and KEGG database were used to analyze the relationship between metabolites, metabolic pathways and diseases.

Results

The external features and ¹H-NMR analysis showed that the sex of rats had no obvious effect on the drug action. A total of 15 potential differential metabolites and six metabolic pathways were screened out through data pattern recognition. Through network analysis and KEGG pathway analysis, three target diseases closely related to differential metabolites were found, and the metabolic pathway related to lung cancer was the central carbon metabolism of cancer.

Conclusion

This study shows that Aconiti Radix Cocta (ARC) may regulate the energy metabolism of the body by influencing arginine synthesis, so as to play the roles of anti-inflammation, analgesia, anti-tumor and immune regulation.

NMR and Metabolomics-A Roadmap for the Future

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

An improved NMR approach for metabolomics of intact serum samples

NMR metabolomics has inherent capabilities for studying biofluids, such as reproducibility, minimal sample preparation, non-destructiveness, and molecular structure elucidation; however, reliable quantitation of metabolites is still a challenge because of the complex matrix of the samples. The serum is one of the most common samples in clinical studies but possibly the most difficult for NMR analysis because of the high content of proteins, which hampers the detection and quantification of metabolites. Different processes for protein removal, such as ultrafiltration and precipitation, have been proposed, but require sample manipulation, increase time and cost, and possibly lead to loss of information in the metabolic profile. Alternative methods that rely on filtering protein signals by NMR pulse sequencing are commonly used, but standardisation of acquisition parameters and spectra calibration is far from being reached. The present technical note is a critical assessment of the sparsely suggested calibrants, pulse sequences and acquisition parameters toward an optimised combination of the three for accurate and reproducible quantification of metabolites in intact serum.

UHPLC/MS-Based Serum Metabolomics Reveals the Mechanism of Radiation-Induced Thrombocytopenia in Mice

Radiation-induced thrombocytopenia is a common and life-threatening side effect of ionizing radiation (IR) therapy. However, the underlying pathological mechanisms remain unclear. In the present study, irradiation was demonstrated to significantly reduce platelet levels, inhibit megakaryocyte differentiation, and promote the apoptosis of bone marrow (BM) cells. A metabolomics approach and a UHPLC-QTOF MS system were subsequently employed for the comprehensive analysis of serum metabolic profiles of normal and irradiated mice. A total of 66 metabolites were significantly altered, of which 56 were up-regulated and 10 were down-regulated in irradiated mice compared to normal mice on day 11 after irradiation. Pathway analysis revealed that disorders in glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, sphingolipid metabolism, inositol phosphate metabolism, and tryptophan metabolism were involved in radiation-induced thrombocytopenia. In addition, three important differential metabolites, namely L-tryptophan, LysoPC (17:0), and D-sphinganine, which were up-regulated in irradiated mice, significantly induced the apoptosis of K562 cells. L-tryptophan inhibited megakaryocyte differentiation of K562 cells. Finally, serum metabolomics was performed on day 30 (i.e., when the platelet levels in irradiated mice recovered to normal levels). The contents of L-tryptophan, LysoPC (17:0), and D-sphinganine in normal and irradiated mice did not significantly differ on day 30 after irradiation. In conclusion, radiation can cause metabolic disorders, which are highly correlated with the apoptosis of hematopoietic cells and inhibition of megakaryocyte differentiation, ultimately resulting in thrombocytopenia. Further, the metabolites, L-tryptophan, LysoPC (17:0), and D-sphinganine can serve as biomarkers for radiation-induced thrombocytopenia.

Multi-Tissue Time-Domain NMR Metabolomics Investigation of Time-Restricted Feeding in Male and Female Nile Grass Rats

Metabolic disease resulting from overnutrition is prevalent and rapidly increasing in incidence in modern society. Time restricted feeding (TRF) dietary regimens have recently shown promise in attenuating some of the negative metabolic effects associated with chronic nutrient stress. The purpose of this study is to utilize a multi-tissue metabolomics approach using nuclear magnetic resonance (NMR) spectroscopy to investigate TRF and sex-specific effects of high-fat diet in a diurnal Nile grass rat model. Animals followed a six-week dietary protocol on one of four diets: chow ad libitum, high-fat ad libitum (HF-AD), high-fat early TRF (HF-AM), or high-fat late TRF (HF-PM), and their liver, heart, and white adipose tissues were harvested at the end of the study and were analyzed by NMR. Time-domain complete reduction to amplitude–frequency table (CRAFT) was used to semi-automate and systematically quantify metabolites in liver, heart, and adipose tissues while minimizing operator bias. Metabolite profiling and statistical analysis revealed lipid remodeling in all three tissues and ectopic accumulation of cardiac and hepatic lipids for HF-AD feeding compared to a standard chow diet. Animals on TRF high-fat diet had lower lipid levels in the heart and liver compared to the ad libitum group; however, no significant differences were noted for adipose tissue. Regardless of diet, females exhibited greater amounts of hepatic lipids compared to males, while no consistent differences were shown in adipose and heart. In conclusion, this study demonstrates the feasibility of performing systematic and time-efficient multi-tissue NMR metabolomics to elucidate metabolites involved in the crosstalk between different metabolic tissues and provides a more holistic approach to better understand the etiology of metabolic disease and the effects of TRF on metabolic profiles.

1H qNMR-Based Metabolomics Discrimination of Covid-19 Severity

The coronavirus disease 2019 (Covid-19), which caused respiratory problems in many patients worldwide, led to more than 5 million deaths by the end of 2021. Experienced symptoms vary from mild to severe illness. Understanding the infection severity to reach a better prognosis could be useful to the clinics, and one study area to fulfill one piece of this biological puzzle is metabolomics. The metabolite profile and/or levels being monitored can help predict phenotype properties. Therefore, this study evaluated plasma metabolomes of 110 individual samples, 57 from control patients and 53 from recent positive cases of Covid-19 (IgM 98% reagent), representing mild to severe symptoms, before any clinical intervention. Polar metabolites from plasma samples were analyzed by quantitative 1H NMR. Glycerol, 3-aminoisobutyrate, formate, and glucuronate levels showed alterations in Covid-19 patients compared to those in the control group (Tukey's HSD p-value cutoff = 0.05), affecting the lactate, phenylalanine, tyrosine, and tryptophan biosynthesis and d-glutamine, d-glutamate, and glycerolipid metabolisms. These metabolic alterations show that SARS-CoV-2 infection led to disturbance in the energetic system, supporting the viral replication and corroborating with the severe clinical conditions of patients. Six polar metabolites (glycerol, acetate, 3-aminoisobutyrate, formate, glucuronate, and lactate) were revealed by PLS-DA and predicted by ROC curves and ANOVA to be potential prognostic metabolite panels for Covid-19 and considered clinically relevant for predicting infection severity due to their straight roles in the lipid and energy metabolism. Thus, metabolomics from samples of Covid-19 patients is a powerful tool for a better understanding of the disease mechanism of action and metabolic consequences of the infection in the human body and may corroborate allowing clinicians to intervene quickly according to the needs of Covid-19 patients.

COLMARq: A Web Server for 2D NMR Peak Picking and Quantitative Comparative Analysis of Cohorts of Metabolomics Samples

Highly quantitative metabolomics studies of complex biological mixtures are facilitated by the resolution enhancement afforded by 2D NMR spectra such as 2D ¹³C-¹H HSQC spectra. Here, we describe a new public web server, COLMARq, for the semi-automated analysis of sets of 2D HSQC spectra of cohorts of samples. The workflow of COLMARq includes automated peak picking using the deep neural network DEEP Picker, quantitative cross-peak volume extraction by numerical fitting using Voigt Fitter, the matching of corresponding cross-peaks across cohorts of spectra, peak volume normalization between different spectra, database query for metabolite identification, and basic univariate and multivariate statistical analyses of the results. COLMARq allows the analysis of cross-peaks that belong to both known and unknown metabolites. After a user has uploaded cohorts of 2D ¹³C-¹H HSQC and optionally 2D ¹H-¹H TOCSY spectra in their preferred format, all subsequent steps on the web server can be performed fully automatically, allowing manual editing if needed and the sessions can be saved for later use. The accuracy, versatility, and interactive nature of COLMARq enables quantitative metabolomics analysis, including biomarker identification, of a broad range of complex biological mixtures as is illustrated for cohorts of samples from bacterial cultures of Pseudomonas aeruginosa in both its biofilm and planktonic states.

NMR based metabolic profiling of patients with potential celiac disease elucidating early biochemical changes of gluten-sensitivity: A pilot study

BACKGROUND

The patients with positive celiac disease (CeD) specific serology, but no evidence of intestinal inflammation are defined as potential celiac disease (PCeD) patients. About one-third of PCeD patients develop intestinal inflammation over time. The present study investigated the metabolome of small intestinal biopsies, blood plasma, and urine of patients with PCeD to understand the biochemical changes underlying the CeD.

METHODS

The metabolic profiles of small intestinal biopsies, blood plasma, and urine of patients with PCeD (n = 7) were compared with CeD (n = 64) and controls (n = 15) [disease controls (DC) and healthy controls (HC)] using 1H NMR spectroscopy.

RESULTS

The intestinal mucosa of PCeD showed lower levels of histidine, glycine, tyrosine, and tryptophan compared to DC. Altered levels of 6 metabolites (glucose, acetate, acetoacetate, β-hydroxybutyrate, pyruvate, arginine) in blood plasma and two metabolites (succinate and aminohippurate) in urine were observed in PCeD compared to HC. The PLS-DA model built on the concentration of blood plasma showed separate clustering for PCeD and CeD patients.

CONCLUSION

Altered metabolic profile of PCeD suggested that gluten intolerance was evident at the metabolic level before the intestinal damage. Altered energy metabolism and lower cytoprotective activity (histidine, glycine, arginine) indicated vulnerability to develop intestinal inflammation in PCeD over time. Our study may provide an insight into early biochemical processes of the progression of PCeD to CeD.

Urinary Metabolomic Study in a Healthy Children Population and Metabolic Biomarker Discovery of Attention-Deficit/Hyperactivity Disorder (ADHD)

Objectives

Knowledge of the urinary metabolomic profiles of healthy children and adolescents plays a promising role in the field of pediatrics. Metabolomics has also been used to diagnose disease, discover novel biomarkers, and elucidate pathophysiological pathways. Attention-deficit/hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in childhood. However, large-sample urinary metabolomic studies in children with ADHD are relatively rare. In this study, we aimed to identify specific biomarkers for ADHD diagnosis in children and adolescents by urinary metabolomic profiling.

Methods

We explored the urine metabolome in 363 healthy children aged 1-18 years and 76 patients with ADHD using high-resolution mass spectrometry.

Results

Metabolic pathways, such as arachidonic acid metabolism, steroid hormone biosynthesis, and catecholamine biosynthesis, were found to be related to sex and age in healthy children. The urinary metabolites displaying the largest differences between patients with ADHD and healthy controls belonged to the tyrosine, leucine, and fatty acid metabolic pathways. A metabolite panel consisting of FAPy-adenine, 3-methylazelaic acid, and phenylacetylglutamine was discovered to have good predictive ability for ADHD, with a receiver operating characteristic area under the curve (ROC-AUC) of 0.918. A panel of FAPy-adenine, N-acetylaspartylglutamic acid, dopamine 4-sulfate, aminocaproic acid, and asparaginyl-leucine was used to establish a robust model for ADHD comorbid tic disorders and controls with an AUC of 0.918.

Elevated Circulatory Proline to Glutamine Ratio (PQR) in Endometriosis and Its Potential as a Diagnostic Biomarker

Endometriosis (EM) is a hormone-dependent gynecological disease associated with chronic pelvic pain and altered immuno-inflammatory processes. It shares some cancer-like characteristics such as increased proline biosynthesis and activated glutaminolysis. Both proline and glutamine are interconvertible metabolically, and studies have shown their roles in cancer cell metabolic reprogramming, redox homeostasis, occurrence/development of endometrial carcinoma, and its further progression toward the malignant state. So based on this, we hypothesized that the circulatory proline to glutamine ratio (PQR) would be altered in EM and may serve as an indicative biomarker to improve the clinical diagnosis of EM. In present study, the circulatory-PQR levels were estimated for 39 EM patients and 48 age matched healthy female subjects using 800 MHz NMR spectroscopy. Among 39 EM patients, 15 were in the clinical stages I to II and referred to here as moderate EM (MEM) patients and 24 were in the clinical stages III to IV and referred here as severe EM (SEM) patients. The circulatory-PQR levels were significantly increased in EM patients (0.99 ± 0.13 μM in MEM; 1.39 ± 0.22 μM in SEM) compared to normal control (NC) subjects (0.52 ± 0.05 μM in NC). Further, the circulatory PQR levels exhibit the highest diagnostic potential with area under receiver operating characteristic (AUROC) curve values equal to 0.87 ± 0.04 [95%CI = 0.79-0.96] for MEM and 0.89 ± 0.04 [95% CI = 0.82-0.96] for SEM. These results suggested that circulatory-PQR has significant potential to serve as a noninvasive biomarker for diagnostic/prognostic screening of EM and further underscored the importance of these two nonessential amino acids (proline and glutamine) in cancer metabolism.

Physico-Chemical Properties and Principal Component Analysis of Biobased Thermosets Developed with Different Batches of Industrial Humins

Humins have already shown their potential as thermosetting resins to produce crosslinked networks and composites, with a large variety of properties depending on the used macromolecular approach. Our group has shown that a very interesting class of materials with tunable flexibility can be made by humins co-polymerization with glycerol diglycidyl ether (GDE). To create a clearer picture on structure-reactivity-properties-application interdependent relationship, a principal component analysis (PCA) was applied on several humins batches. The PCA allowed to obtain a clear discrimination between the humins/GDE resins samples in 3 groups which correlate very well with the results of copolymerization reactivity (DSC) and thermosets properties: crosslink density, thermal stability, tan δ, Shore D hardness values, etc.

Marine Dissolved Organic Matter Shares Thousands of Molecular Formulae Yet Differs Structurally across Major Water Masses

Most oceanic dissolved organic matter (DOM) is still not fully molecularly characterized. We combined high-field nuclear magnetic resonance (NMR) and ultrahigh-resolution mass spectrometry (Fourier-transform ion cyclotron resonance mass spectrometry, FT-ICR-MS) for the structural and molecular formula-level characterization of solid-phase extracted (SPE) DOM from surface, mesopelagic, and bathypelagic Atlantic and Pacific Ocean samples. Using a MicroCryoProbe, unprecedented low amounts of SPE-DOM (∼1 mg carbon) were sufficient for two-dimensional NMR analysis. Low proportions of olefinic and aromatic relative to aliphatic and carboxylated structures (NMR) at the sea surface were likely related to photochemical transformations. This was consistent with lower molecular masses and higher degrees of saturation and oxygenation (FT-ICR-MS) compared to those of the deep sea. Carbohydrate structures in the mesopelagic North Pacific Ocean suggest export and release from sinking particles. In our sample set, the universal molecular DOM composition, as captured by FT-ICR-MS, appears to be structurally more diverse when analyzed by NMR, suggesting DOM variability across oceanic provinces to be more pronounced than previously assumed. As a proof of concept, our study takes advantage of new complementary approaches resolving thousands of structural and molecular DOM features while applying reasonable instrument times, allowing for the analysis of large oceanic data sets to increase our understanding of marine DOM biogeochemistry.

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

A Real-Life Reproducibility Assessment for NMR Metabolomics

Nuclear magnetic resonance (NMR) metabolomics is currently popular enough to attract both specialized and non-specialized NMR groups involving both analytical trained personnel and newcomers, including undergraduate students. Recent interlaboratory studies performed by established NMR metabolomics groups demonstrated high reproducibility of the state-of-the-art NMR equipment and SOPs. There is, however, no assessment of NMR reproducibility when mixing both analytical experts and newcomers. An interlaboratory assessment of NMR quantitation reproducibility was performed using two NMR instruments belonging to different laboratories and involving several operators with different backgrounds and metabolomics expertise for the purpose of assessing the limiting factors for data reproducibility in a multipurpose NMR environment. The variability induced by the operator, automatic pipettes, NMR tubes and NMR instruments was evaluated in order to assess the limiting factors for quantitation reproducibility. The results estimated the expected reproducibility data in a real-life multipurpose NMR laboratory to a maximum 4% variability, demonstrating that the current NMR equipment and SOPs may compensate some of the operator-induced variability.

Multivariate analysis of NMR-based metabolomic data

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.

NMR spectroscopy spotlighting immunogenicity induced by COVID-19 vaccination to mitigate future health concerns

In this review, the disease and immunogenicity affected by COVID-19 vaccination at the metabolic level are described considering the use of nuclear magnetic resonance (NMR) spectroscopy for the analysis of different biological samples. Consistently, we explain how different biomarkers can be examined in the saliva, blood plasma/serum, bronchoalveolar-lavage fluid (BALF), semen, feces, urine, cerebrospinal fluid (CSF) and breast milk. For example, the proposed approach for the given samples can allow one to detect molecular biomarkers that can be relevant to disease and/or vaccine interference in a system metabolome. The analysis of the given biomaterials by NMR often produces complex chemical data which can be elucidated by multivariate statistical tools, such as PCA and PLS-DA/OPLS-DA methods. Moreover, this approach may aid to improve strategies that can be helpful in disease control and treatment management in the future.

•

NMR analysis of various bio-samples can explore disease course and vaccine interaction.

•

Immunogenicity and reactogenicity caused by COVID-19 vaccination can be studied by NMR.

•

Vaccine interaction alters metabolic pathway(s) at a certain stage, and this mechanism can be probed at the metabolic level.

Human metabolite detection by surface-enhanced Raman spectroscopy

Metabolites are important biomarkers in human body fluids, conveying direct information of cellular activities and physical conditions. Metabolite detection has long been a research hotspot in the field of biology and medicine. Surface-enhanced Raman spectroscopy (SERS), based on the molecular “fingerprint” of Raman spectrum and the enormous signal enhancement (down to a single-molecule level) by plasmonic nanomaterials, has proven to be a novel and powerful tool for metabolite detection. SERS provides favorable properties such as ultra-sensitive, label-free, rapid, specific, and non-destructive detection processes. In this review, we summarized the progress in recent 10 years on SERS-based sensing of endogenous metabolites at the cellular level, in tissues, and in biofluids, as well as drug metabolites in biofluids. We made detailed discussions on the challenges and optimization methods of SERS technique in metabolite detection. The combination of SERS with modern biomedical technology were also anticipated.

Quantitation of Nuclear Magnetic Resonance Spectra at Earth's Magnetic Field

The inherently quantitative nature of nuclear magnetic resonance (NMR) spectroscopy is one of the most attractive aspects of this analytical technique. Quantitative NMR analyses have typically been limited to high-field (>1 T) applications. The aspects for quantitation at low magnetic fields (<1 mT) have not been thoroughly investigated and are shown to be impacted by the complex signatures that arise at these fields from strong heteronuclear J-couplings. This study investigates quantitation at Earth's magnetic field (∼50 μT) for a variety of samples in strongly, weakly, and uncoupled spin systems. To achieve accurate results in this regime, the instrumentation, experimental acquisition, processing, and theoretical aspects must be considered and reconciled. Of particular note is the constant field nuclear receptivity equation, which has been re-derived in this study to account for strong coupling and quality factor effects. The results demonstrate that the quantitation of homonuclear molecular groups, determination of heteronuclear pseudoempirical formulas, and mixture analysis are all feasible at Earth's magnetic field in a greatly simplified experimental system.

Metabolomics Insights into Inflammatory Bowel Disease: A Comprehensive Review

Inflammatory bowel disease (IBD) is a chronic, complex relapsing disorder characterised by immune dysregulation, gut microbiota alteration, and disturbed intestinal permeability. The diagnosis and the management of IBD are challenging due to the recurrent nature and complex evolution of the disease. Furthermore, the molecular mechanism underlying the aetiology and pathogenesis of IBD is still poorly understood. There is an unmet need for novel, reliable, and noninvasive tools for diagnosing and monitoring IBD. In addition, metabolomic profiles may provide a priori determination of optimal therapeutics and reveal novel targets for therapies. This review tries to gather scientific evidence to summarise the emerging contribution of metabolomics to elucidate the mechanisms underlying IBD and changes associated with disease phenotype and therapies, as well as to identify biomarkers with metabolic imbalance in those patients. Metabolite changes during health and disease could provide insights into the disease pathogenesis and the discovery of novel indicators for the diagnosis and prognosis assessment of IBD. Metabolomic studies in IBD have shown changes in tricarboxylic acid cycle intermediates, amino-acid and fatty-acid metabolism, and oxidative pathways. Metabolomics has made progress towards identifying metabolic alterations that may provide clinically useful biomarkers and a deeper understanding of the disease. However, at present, there is insufficient evidence evaluating the predictive accuracy of these molecular signatures and their diagnostic ability, which is necessary before metabolomic data can be translated into clinical practice.

Variable-temperature NMR spectroscopy for metabolite identification in biological materials

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 1H NMR, suffers from low dispersion of resonance frequencies, which often hampers the identification of metabolites. In this article we show that 1H NMR spectra contain previously overlooked parameters potentially helpful in metabolite identification, namely the rates of temperature-induced changes of chemical shifts. We prove that they are reproducible between various metabolite mixtures and can be determined quickly when Radon transform is used to process the data.

Research Progress of NMR in Natural Product Quantification

In the fields of medicine and health, traditional high-performance liquid chromatography or UV-visible spectrophotometry is generally used for substance quantification. However, over time, nuclear magnetic resonance spectroscopy (NMR) has gradually become more mature. Nuclear magnetic resonance spectroscopy has certain advantages in the quantitative analysis of substances, such as being nondestructive, having a high flux and short analysis time. Nuclear magnetic resonance spectroscopy has been included in the pharmacopoeiae of various countries. In this paper, the principle of nuclear magnetic resonance spectroscopy and the recent progress in the quantitative study of natural products by NMR are reviewed, and its application in the quantitative study of natural products is proposed. At the same time, the problems of using NMR alone to quantify natural products are summarized and corresponding suggestions are put forward.

NMR Metabolite Profiles of the Bivalve Mollusc Mytilus galloprovincialis Before and After Immune Stimulation With Vibrio splendidus

The hemolymph metabolome of Mytilus galloprovincialis injected with live Vibrio splendidus bacteria was analyzed by ¹H-NMR spectrometry. Changes in spectral hemolymph profiles were already detected after mussel acclimation (3 days at 18 or 25 °C). A significant decrease of succinic acid was accompanied by an increase of most free amino acids, mytilitol, and, to a smaller degree, osmolytes. These metabolic changes are consistent with effective osmoregulation, and the restart of aerobic respiration after the functional anaerobiosis occurred during transport. The injection of Vibrio splendidus in mussels acclimated at 18°C caused a significant decrease of several amino acids, sugars, and unassigned chemical species, more pronounced at 24 than at 12 h postinjection. Correlation heatmaps indicated dynamic metabolic adjustments and the relevance of protein turnover in maintaining the homeostasis during the response to stressful stimuli. This study confirms NMR-based metabolomics as a feasible analytical approach complementary to other omics techniques in the investigation of the functional mussel responses to environmental challenges.

Grading of endometrial cancer using 1H HR-MAS NMR-based metabolomics

The tissue metabolomic characteristics associated with endometrial cancer (EC) at different grades were studied using high resolution (400 MHz) magic angle spinning (HR-MAS) proton spectroscopy. The metabolic profiles were obtained from 64 patients (14 with grade 1 (G1), 33 with grade 2 (G2) and 17 with grade 3 (G3) tumors) and compared with the profile acquired from 10 patients with the benign disorders. OPLS-DA revealed increased valine, isoleucine, leucine, hypotaurine, serine, lysine, ethanolamine, choline and decreased creatine, creatinine, glutathione, ascorbate, glutamate, phosphoethanolamine and scyllo-inositol in all EC grades in reference to the non-transformed tissue. The increased levels of taurine was additionally detected in the G1 and G2 tumors in comparison to the control tissue, while the elevated glycine, N-acetyl compound and lactate—in the G1 and G3 tumors. The metabolic features typical for the G1 tumors are the increased dimethyl sulfone, phosphocholine, and decreased glycerophosphocholine and glutamine levels, while the decreased myo-inositol level is characteristic for the G2 and G3 tumors. The elevated 3-hydroxybutyrate, alanine and betaine levels were observed in the G3 tumors. The differences between the grade G1 and G3 malignances were mainly related to the perturbations of phosphoethanolamine and phosphocholine biosynthesis, inositol, betaine, serine and glycine metabolism. The statistical significance of the OPLS-DA modeling was also verified by an univariate analysis. HR-MAS NMR based metabolomics provides an useful insight into the metabolic reprogramming in endometrial cancer.

Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor

Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (¹H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel ¹H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the ¹H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).

Metabolic Phenotypes in Asthmatic Adults: Relationship with Inflammatory and Clinical Phenotypes and Prognostic Implications

Bronchial asthma is a chronic disease that affects individuals of all ages. It has a high prevalence and is associated with high morbidity and considerable levels of mortality. However, asthma is not a single disease, and multiple subtypes or phenotypes (clinical, inflammatory or combinations thereof) can be detected, namely in aggregated clusters. Most studies have characterised asthma phenotypes and clusters of phenotypes using mainly clinical and inflammatory parameters. These studies are important because they may have clinical and prognostic implications and may also help to tailor personalised treatment approaches. In addition, various metabolomics studies have helped to further define the metabolic features of asthma, using electronic noses or targeted and untargeted approaches. Besides discriminating between asthma and a healthy state, metabolomics can detect the metabolic signatures associated with some asthma subtypes, namely eosinophilic and non-eosinophilic phenotypes or the obese asthma phenotype, and this may prove very useful in point-of-care application. Furthermore, metabolomics also discriminates between asthma and other “phenotypes” of chronic obstructive airway diseases, such as chronic obstructive pulmonary disease (COPD) or Asthma–COPD Overlap (ACO). However, there are still various aspects that need to be more thoroughly investigated in the context of asthma phenotypes in adequately designed, homogeneous, multicentre studies, using adequate tools and integrating metabolomics into a multiple-level approach.

Biomarker Discovery in Atherosclerotic Diseases Using Quantitative Nuclear Magnetic Resonance Metabolomics

Despite great progress in the management of atherosclerosis (AS), its subsequent cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. This is probably due to insufficient risk detection using routine lipid testing; thus, there is a need for more effective approaches relying on new biomarkers. Quantitative nuclear magnetic resonance (qNMR) metabolomics is able to phenotype holistic metabolic changes, with a unique advantage in regard to quantifying lipid-protein complexes. The rapidly increasing literature has indicated that qNMR-based lipoprotein particle number, particle size, lipid components, and some molecular metabolites can provide deeper insight into atherogenic diseases and could serve as novel promising determinants. Therefore, this article aims to offer an updated review of the qNMR biomarkers of AS and CVD found in epidemiological studies, with a special emphasis on lipoprotein-related parameters. As more researches are performed, we can envision more qNMR metabolite biomarkers being successfully translated into daily clinical practice to enhance the prevention, detection and intervention of atherosclerotic diseases.

Metabolomic Studies for the Evaluation of Toxicity Induced by Environmental Toxicants on Model Organisms

Environmental pollution causes significant toxicity to ecosystems. Thus, acquiring a deeper understanding of the concentration of environmental pollutants in ecosystems and, clarifying their potential toxicities is of great significance. Environmental metabolomics is a powerful technique in investigating the effects of pollutants on living organisms in the environment. In this review, we cover the different aspects of the environmental metabolomics approach, which allows the acquisition of reliable data. A step-by-step procedure from sample preparation to data interpretation is also discussed. Additionally, other factors, including model organisms and various types of emerging environmental toxicants are discussed. Moreover, we cover the considerations for successful environmental metabolomics as well as the identification of toxic effects based on data interpretation in combination with phenotype assays. Finally, the effects induced by various types of environmental toxicants in model organisms based on the application of environmental metabolomics are also discussed.

Nuclear Magnetic Resonance Spectroscopy: A Multifaceted Toolbox to Probe Structure, Dynamics, Interactions, and Real-Time In Situ Release Kinetics in Peptide-Liposome Formulations

Liposomal formulations represent attractive biocompatible and tunable drug delivery systems for peptide drugs. Among the tools to analyze their physicochemical properties, nuclear magnetic resonance (NMR) spectroscopy, despite being an obligatory technique to characterize molecular structure and dynamics in chemistry as well as in structural biology, yet appears to be rather sparsely used to study drug-liposome formulations. In this work, we exploited several facets of liquid-state NMR spectroscopy to characterize liposomal delivery systems for the apelin-derived K14P peptide and K14P modified by Nα-fatty acylation. Various liposome compositions and preparation modes were analyzed. Using NMR, in combination with cryo-electron microscopy and dynamic light scattering, we determined structural, dynamic, and self-association properties of these peptides in solution and probed their interactions with liposomes. Using ³¹P and ¹H NMR, we characterized membrane fluidity and thermotropic phase transitions in empty and loaded liposomes. Based on diffusion and ¹H NMR experiments, we localized and quantified peptides with respect to the interior/exterior of liposomes and changes over time and upon thermal treatments. Finally, we assessed the release kinetics of several solutes and compared various formulations. Taken together, this work shows that NMR has the potential to assist the design of peptide/liposome systems and more generally drug delivery systems.

Special Issue: NMR-Based Metabolomics

Nuclear magnetic resonance (NMR) spectroscopy remains one of the core analytical platforms for metabolomics, providing complementary chemical information to others, such as mass spectrometry, and offering particular advantages in some areas of research on account of its inherent robustness, reproducibility, and phenomenal dynamic range [...].

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.

Automatic 1D 1H NMR Metabolite Quantification for Bioreactor Monitoring

High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.

Quantitative NMR as a Versatile Tool for the Reference Material Preparation

The assessment of primary calibrator purity is critical for establishing traceability to the International System of Units (SI). Recently, quantitative nuclear magnetic resonance (qNMR) has been used as a purity determination method for reference material development, and many related measurement techniques have been designed to acquire accurate and reliable results. This review introduces the recent advances in these techniques (including multidimensional methods), focusing on the application of qNMR to reference material preparation.