High-Throughput Metabolomics by 1D NMR

Novel NMR-Based Approach to Reveal the 'Metabolic Fingerprint' of Cytotoxic Gold Drugs in Cancer Cells

A combination of pathway enrichment and metabolite clustering analysis is used to interpret untargeted 1H NMR metabolomics data, enabling a biochemically informative comparison of the effects induced by a panel of known cytotoxic gold(I) and gold(III) compounds in A2780 ovarian cancer cells. The identification of the most dysregulated pathways for the major classes of compounds highlights specific chemical features that lead to common biological effects. The proposed approach may have broader applicability to the screening of metal-based drug candidate libraries, which is always complicated by their multitarget nature, and support the comprehensive interpretation of their metabolic actions.

Pure Shift NMR with Solvent Suppression: A Robust and General Method for Determining Quantitative Metabolic Profiles in Biofluids

Ultrahigh-resolution pure shift NMR has recently been shown as a promising approach for providing quantitative metabolic profiles that can be used to study the metabolic footprint left by cancer cells in their aqueous growth medium. In this approach, a library of reference 1H pure shift spectra with water suppression was implemented to determine metabolite concentrations from the NOESY-presat-PSYCHE-SAPPHIRE spectrum recorded on the extracellular medium. This achievement clearly called for a generalization of a quantification method relying on ultrahigh-resolution data to other biological samples of interest (urine, plasma, tissue extracts, etc.), which requires evaluating the robustness of the analytical workflow. We have first addressed the influence of sample preparation on the quality of metabolite quantification. The quantification performed on a model mixture of metabolites prepared under different conditions shows good linearity, trueness, and precision, which highlights the high reproducibility of the proposed analytical protocol regardless of the physicochemical conditions in the sample. Second, we have successfully implemented this quantification protocol to determine metabolite levels in real urine and plasma samples, thereby paving the way for the use of the library of pure shift reference spectra for accurate and quantitative metabolic profiling of a broad range of aqueous samples.

Validating Centralized Biobanking Workflows for NMR Metabolomics Using the PRIMA Panel

The quality of biological samples used in metabolomics research is significantly influenced by preanalytical factors, such as the timing of centrifugation and freezing. This study aimed to evaluate how preanalytical factors, like delays in centrifugation and freezing, affect metabolomics research. Blood samples, collected in various tube types, were subjected to controlled pre- and postcentrifugation delays. Metabolite levels were quantified using NMR spectroscopy and fitted in linear mixed models used to predict changes in metabolite concentrations over time. The results showed that some metabolites, such as lactic acid, were significantly affected by even short delays, while others remained stable for longer. The study introduced the concept of a "stability time point", marking when a metabolite's concentration changes by 20%. These predictive models were validated in a separate cohort. To apply these findings, the authors developed the PRIMA Panel, an open-source R Shiny tool. This tool allows researchers to assess the impact of preanalytical variations on their samples, predict metabolite stability, and generate performance reports. The PRIMA Panel was tested using samples from the Dresden Integrated Liquid Biobank, proving its utility in a real-world biobank setting. The study emphasizes the importance of tracking preanalytical factors to improve the reliability of metabolomics analyses. The PRIMA Panel is available online and for local deployment, providing a practical solution for quality control in metabolomics research. The results of the study underscore the importance of tracking preanalytical factors in biobanking. A versatile tool for assessing their impact on metabolic data is introduced, improving the reliability of future analyses.

Development of an In Situ G Protein-Coupled Receptor Fragment Molecule Screening Approach with High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance

Small molecules are essential for investigating the pharmacology of membrane proteins and remain the most common approach for therapeutically targeting them. However, most experimental small molecule screening methods require ligands containing radiolabels or fluorescent labels and often involve isolating proteins from their cellular environment. Additionally, most conventional screening methods are suited for identifying compounds with moderate to higher affinities (KD < 1 μM) and are less effective at detecting lower affinity compounds, such as weakly binding molecular fragments. To address these limitations, we demonstrated a proof-of-concept application of high-resolution magic angle spinning nuclear magnetic resonance (HRMAS NMR) spectroscopy with small molecules that bind the human A2A adenosine receptor (A2AAR), a class A G protein-coupled receptor. Our approach leverages a streamlined workflow to prepare NMR samples with only milligrams of unpurified cell membranes containing ∼1 μM of A2AAR. Utilizing saturation transfer difference NMR, we identified bound small molecules from spectra recorded within minutes and further derived information on ligand binding poses without the need for detailed structure determination. After establishing optimal criteria for which the HRMAS approach is most sensitive, we leveraged our HRMAS approach to identify and characterize molecular fragments not previously known to be ligands of A2AAR. In molecular docking and simulations, we observed novel binding poses for these fragments, which revealed the potential to grow them into more complex ligands and confirmed HRMAS NMR as a valuable tool for lead compound identification in the context of fragment-based drug discovery.

Artificial neural network detection of pancreatic cancer from proton (1H) magnetic resonance spectroscopy patterns of plasma metabolites

BACKGROUND

Routine screening to detect silent but deadly cancers such as pancreatic ductal adenocarcinoma (PDAC) can significantly improve survival, creating an important need for a convenient screening test. High-resolution proton (1H) magnetic resonance spectroscopy (MRS) of plasma identifies circulating metabolites that can allow detection of cancers such as PDAC that have highly dysregulated metabolism.

METHODS

We first acquired 1H MR spectra of human plasma samples classified as normal, benign pancreatic disease and malignant (PDAC). We next trained a system of artificial neural networks (ANNs) to process and discriminate these three classes using the full spectrum range and resolution of the acquired spectral data. We then identified and ranked spectral regions that played a salient role in the discrimination to provide interpretability of the results. We tested the accuracy of the ANN performance using blinded plasma samples.

RESULTS

We show that our ANN approach yields, in a cross validation-based training of 170 samples, a sensitivity and a specificity of 100% for malignant versus non-malignant (normal and disease combined) discrimination. The trained ANNs achieve a sensitivity and specificity of 87.5% and 93.1% respectively (AUC: ROC = 0.931, P-R = 0.854), with 45 blinded plasma samples. Further, we show that the salient spectral regions of the ANN discrimination correspond to metabolites of known importance for their role in cancers.

CONCLUSIONS

Our results demonstrate that the ANN approach presented here can identify PDAC from 1H MR plasma spectra to provide a convenient plasma-based assay for population-level screening of PDAC. The ANN approach can be suitably expanded to detect other cancers with metabolic dysregulation.

Hyperpolarized 13C NMR Metabolomics of Urine Samples at Natural Abundance Applied to Chronic Kidney Disease

NMR is a central tool in the field of metabolomics, thanks to its ability to provide valuable structural and quantitative information with high precision. Most NMR-based metabolomics studies rely on 1D 1H detection, which is heavily limited by strong peak overlap. 13C NMR benefits from a wider spectral dispersion and narrower signal line width but is barely used in metabolomics due to its low sensitivity. Dissolution dynamic nuclear polarization (d-DNP) offers an opportunity to improve 13C NMR sensitivity by several orders of magnitude. Here, we show that this emerging hyperpolarized metabolomics approach can provide meaningful information about clinical samples. Achieving sub-mM limits of detection with 13C at natural abundance in urine samples was made possible by a meticulous design of the experimental workflow. The analysis of human urine samples from patients with different stages of chronic kidney disease (CKD) was performed using 13C d-DNP NMR and benchmarked to conventional 1H NMR metabolomics at a high magnetic field to explore the complementarity between the two methods. Multivariate analysis of the d-DNP 13C NMR dataset provided a statistical model able to distinguish patients with CKD from control patients. Moreover, 13C d-DNP NMR spectra highlighted several biomarkers known to be biologically relevant. Some of them were in agreement with those obtained with conventional 1H NMR, and the results also highlighted the complementarity of biomarker coverage between hyperpolarized and conventional NMR metabolomics. In particular, 13C hyperpolarized NMR allowed the annotation of two biomarkers that could not be detected by 1H NMR because of peak overlap (i.e., guanine and guanidoacetate).

Decoding aging clocks: New insights from metabolomics

Chronological age is a crucial risk factor for diseases and disabilities among older adults. However, individuals of the same chronological age often exhibit divergent biological aging states, resulting in distinct individual risk profiles. Chronological age estimators based on omics data and machine learning techniques, known as aging clocks, provide a valuable framework for interpreting molecular-level biological aging. Metabolomics is an intriguing and rapidly growing field of study, involving the comprehensive profiling of small molecules within the body and providing the ultimate genome-environment interaction readout. Consequently, leveraging metabolomics to characterize biological aging holds immense potential. The aim of this review was to provide an overview of metabolomics approaches, highlighting the establishment and interpretation of metabolomic aging clocks while emphasizing their strengths, limitations, and applications, and to discuss their underlying biological significance, which has the potential to drive innovation in longevity research and development.

"Raman plus X" dual-modal spectroscopy technology for food analysis: A review

Raman spectroscopy, a nondestructive optical technique that provides detailed chemical information, has attracted growing interest in the food industry. Complementary spectroscopic methods, such as near-infrared (NIR) spectroscopy, nuclear magnetic resonance (NMR), terahertz (THz) spectroscopy, laser-induced breakdown spectroscopy (LIBS), and fluorescence spectroscopy (Flu), enhance Raman spectroscopy's capabilities in various applications. The integration of Raman with these techniques, termed "Raman plus X," has shown significant potential in agri-food analysis. This review highlights the latest advances and applications of dual-modal spectroscopy methods combining Raman spectroscopy with NIR, NMR, THz, LIBS, and Flu in food analysis. Key applications include detecting harmful contaminants, evaluating food quality, identifying adulteration, and characterizing structure. The synergistic use of Raman-based dual-modal spectroscopy provides more comprehensive information and improves modeling accuracy compared to single techniques. The review also explores the role of data fusion in multisource spectral analysis and discusses challenges and prospects of "Raman plus X," including the development of integrated hardware and advanced data fusion algorithms. These advancements aim to streamline multisource data analysis, offering valuable insights to select appropriate analytical methods for practical applications in the food industry.

Untargeted LC-HRMS analyses reveal metabolomic specificities between wine yeast strains selected for their malic acid production

The alcoholic fermentation of wine is mostly achieved by the species Saccharomyces cerevisiae that display a large variability for their ability to consume or produce malic acid. To better characterize the metabolism of such group of strains we explored their non-volatile metabolome using an untargeted LC-HRMS approach. The chemical classes and the putative structures of several hundred compounds where annotated using MS2 spectra using the SIRIUS software. By using both supervised and univariate statistical analyses, several metabolic features able to discriminate the two group of strains in several wines were listed. Quantitative enrichment analyses pointed out drastic differences in pantothenic acid metabolism between the two group of strains. In addition, the produced showed important change in their nitrogen composition that might be due to the pH difference in the resulting wines. Altogether this work paves the avenue for better characterizing the biochemical impact of yeast strains that modulate wine acidity.

Comparative lipidomic and metabolomic profiling of mdx and severe mdx-apolipoprotein e-null mice

Despite its notoriously mild phenotype, the dystrophin-deficient mdx mouse is the most common model of Duchenne muscular dystrophy (DMD). By mimicking a human DMD-associated metabolic comorbidity, hyperlipidemia, in mdx mice by inactivating the apolipoprotein E gene (mdx-ApoE) we previously reported severe myofiber damage exacerbation via histology with large fibro-fatty infiltrates and phenotype humanization with ambulation dysfunction when fed a cholesterol- and triglyceride-rich Western diet (mdx-ApoEW). Herein, we performed comparative lipidomic and metabolomic analyses of muscle, liver and serum samples from mdx and mdx-ApoEW mice using solution and high-resolution-magic angle spinning (HR-MAS) 1H-NMR spectroscopy. Compared to mdx and regular chow-fed mdx-ApoE mice, we observed an order of magnitude increase in lipid deposition in gastrocnemius muscle of mdx-ApoEW mice including 11-fold elevations in -CH3 and -CH2 lipids, along with pronounced elevations in serum cholesterol, fatty acid, triglyceride and phospholipids. Hepatic lipids were also elevated but did not correlate with the extent of muscle lipid infiltration or differences in serum lipids. This study provides the first lipometabolomic signature of severe mdx lesions exacerbated by high circulating lipids and lends credence to claims that the liver, the main regulator of whole-body lipoprotein metabolism, may play only a minor role in this process.

Using New Technologies to Analyze Gut Microbiota and Predict Cancer Risk

The gut microbiota significantly impacts human health, influencing metabolism, immunological responses, and disease prevention. Dysbiosis, or microbial imbalance, is linked to various diseases, including cancer. It is crucial to preserve a healthy microbiome since pathogenic bacteria, such as Escherichia coli and Fusobacterium nucleatum, can cause inflammation and cancer. These pathways can lead to the formation of tumors. Recent advancements in high-throughput sequencing, metagenomics, and machine learning have revolutionized our understanding of the role of gut microbiota in cancer risk prediction. Early detection is made easier by machine learning algorithms that improve the categorization of cancer kinds based on microbiological data. Additionally, the investigation of the microbiome has been transformed by next-generation sequencing (NGS), which has made it possible to fully profile both cultivable and non-cultivable bacteria and to understand their roles in connection with cancer. Among the uses of NGS are the detection of microbial fingerprints connected to treatment results and the investigation of metabolic pathways implicated in the development of cancer. The combination of NGS with machine learning opens up new possibilities for creating customized medicine by enabling the development of diagnostic tools and treatments that are specific to each patient's microbiome profile, even in the face of obstacles like data complexity. Multi-omics studies reveal microbial interactions, biomarkers for cancer detection, and gut microbiota's impact on cancer progression, underscoring the need for further research on microbiome-based cancer prevention and therapy.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Basic accuracy of a 1D NOESY with presaturation method using standard solutions of amino and maleic acids

1D NOESY with presaturation (NOESY-presat) is the most popular water suppression method. When D2O solutions of L-phenylalanine or L-valine were measured using NOESY, the absolute concentration biases increased with longer mixing and evolution times, reaching a maximum of 54% with respect to the preparation values. At mixing and evolution times of 0 ms and 0 µs, respectively, the absolute concentration biases were reduced to less than 3%. The remaining biases were caused by the off-resonance effect, which was prevented by setting the frequency offset to an intermediate value between the analyte and internal standard 3-(trimethylsilyl)-1-propanesulfonic acid-d6 (DSS-d6) signals. Nevertheless, NOESY-presat gave maximum absolute biases of 26% and 11% for glycine and maleic acid concentrations, respectively, in three H2O/D2O (90/10 vol%) solutions. The proposed NOESY-dual-presat method reduced the absolute biases to below 4%. However, water suppression was insufficient but was improved by setting the frequency offset to the same as the presaturation offset with the H2O signal, although the absolute biases rose to 5 to 13%. Quantitative analyses using NOESY-presat and NOESY-dual-presat require careful consideration of the off-resonance effect.

COLMAR1d: A Web Server for Automated, Quantitative One-Dimensional Nuclear Magnetic Resonance-Based Metabolomics at Arbitrary Magnetic Fields

The field of metabolomics, which is quintessential in today's omics research, involves the large-scale detection, identification, and quantification of small-molecule metabolites in a wide range of biological samples. Nuclear magnetic resonance spectroscopy (NMR) has emerged as a powerful tool for metabolomics due to its high resolution, reproducibility, and exceptional quantitative nature. One of the key bottlenecks of metabolomics studies, however, remains the accurate and automated analysis of the resulting NMR spectra with good accuracy and minimal human intervention. Here, we present the COLMAR1d platform, consisting of a public web server and an optimized database, for one-dimensional (1D) NMR-based metabolomics analysis to address these challenges. The COLMAR1d database comprises more than 480 metabolites from GISSMO enabling a database query of spectra measured at arbitrary magnetic field strengths, as is demonstrated for spectra acquired between 1H resonance frequencies of 80 MHz and 1.2 GHz of mouse serum, DMEM cell growth medium, and wine. COLMAR1d combines the GISSMO metabolomics database concept with the latest tools for automated processing, spectral deconvolution, database querying, and globally optimized mixture analysis for improved accuracy and efficiency. By leveraging advanced computational algorithms, COLMAR1d offers a user-friendly, automated platform for quantitative 1D NMR-based metabolomics analysis allowing a wide range of applications, including biomarker discovery, metabolic pathway elucidation, and integration with multiomics strategies.

Novel Pseudo-Two-Dimensional 19F NMR Spectroscopy for Rapid Simultaneous Detection of Amines in Complex Mixture

Rapid detection of amines in complex mixtures presents a significant challenge. Here, we introduce a novel nuclear magnetic resonance (NMR) method for amine detection utilizing a probe with two fluorine atoms in distinct chemical environments. Upon interaction with an amine, the probe generates two atomic resonance peaks, which are used to create coordinates, revealing fluorine chemical shifts on the 19F NMR spectroscopy. This innovative approach allows for the clear distinction of amine signals in a two-dimensional plane. This method has been effectively employed in analyzing amines in pharmaceuticals and amino acids in Ophiopogon japonicus and dry white wine, providing a robust and general approach for amine analysis.

Metabolomic profiling of COVID-19 using serum and urine samples in intensive care and medical ward cohorts

The COVID-19 pandemic remains a significant global health threat, with uncertainties persisting regarding the factors determining whether individuals experience mild symptoms, severe conditions, or succumb to the disease. This study presents an NMR metabolomics-based approach, analysing 80 serum and urine samples from COVID-19 patients (34 intensive care patients and 46 hospitalized patients) and 32 from healthy controls. Our research identifies discriminant metabolites and clinical variables relevant to COVID-19 diagnosis and severity. These discriminant metabolites play a role in specific pathways, mainly "Phenylalanine, tyrosine and tryptophan biosynthesis", "Phenylalanine metabolism", "Glycerolipid metabolism" and "Arginine and proline metabolism". We propose a three-metabolite diagnostic panel-comprising isoleucine, TMAO, and glucose-that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Furthermore, we found an optimal biomarker panel capable of efficiently classify disease severity considering both clinical characteristics (obesity/overweight, dyslipidemia, and lymphocyte count) together with metabolites content (ethanol, TMAO, tyrosine and betaine).

Unlocking the Power of Human Ferritin: Enhanced Drug Delivery of Aurothiomalate in A2780 Ovarian Cancer Cells

Aurothiomalate (AuTM) is an FDA-approved antiarthritic gold drug with unique anticancer properties. To enhance its anticancer activity, we prepared a bioconjugate with human apoferritin (HuHf) by attaching some AuTM moieties to surface protein residues. The reaction of apoferritin with excess AuTM yielded a single adduct, that was characterized by ESI MS and ICP-OES analysis, using three mutant ferritins and trypsinization experiments. The adduct contains ~3 gold atoms per ferritin subunit, arranged in a small cluster bound to Cys90 and Cys102. MD simulations provided a plausible structural model for the cluster. The adduct was evaluated for its pharmacological properties and was found to be significantly more cytotoxic than free AuTM against A2780 cancer cells mainly due to higher gold uptake. NMR-metabolomics showed that AuTM bound to HuHf and free AuTM induced qualitatively similar changes in treated cancer cells, indicating that the effects on cell metabolism are approximately the same, in agreement with independent biochemical experiments. In conclusion, we have demonstrated here that a molecularly precise bioconjugate formed between AuTM and HuHf exhibits anticancer properties far superior to the free drug, while retaining its key mechanistic features. Evidence is provided that human ferritin can serve as an excellent carrier for this metallodrug.

Optimizing Skin Surface Metabolomics: A Comprehensive Evaluation of Sampling Methods, Extraction Solvents, and Analytical Techniques

Characterizing the metabolite fingerprint from the skin surface provides invaluable insights into skin biology and microbe-host interactions. To ensure data accuracy and reproducibility, it is essential to develop standard operating procedures for skin surface metabolomics. However, there is a notable lack of studies in this area. In this study, we thoroughly evaluated different sampling materials, extraction solvents, taping methods (frequency and number of tapes), and analytical techniques to optimize skin surface metabolomics. Our results showed that the combination of D-Squame D100 tape with a methyl tert-butyl ether/methanol extractant is optimal for skin surface lipidomics. Performing the skin-taping procedure 5 times with 1 tape yields sufficient biomass for lipid analysis, whereas the optimal taping procedure varies for water-soluble compounds. In addition, our study identified associations among the skin surface metabolites, some of which potentially underlie the formation of microbial cutotypes and offer insights into host-microbe interactions.

Homocysteine contributes to atherogenic transformation of the aorta in rabbits in the absence of hypercholesterolemia

Atherosclerosis, the leading cause of cardiovascular disease, cannot be sufficiently explained by established risk factors, including cholesterol. Elevated plasma homocysteine (Hcy) is an independent risk factor for atherosclerosis and is closely linked to cardiovascular mortality. However, its role in atherosclerosis has not been fully clarified yet. We have previously shown that rabbits fed a diet deficient in B vitamins and choline (VCDD), which are required for Hcy degradation, exhibit an accumulation of macrophages and lipids in the aorta, aortic stiffening and disorganization of aortic collagen in the absence of hypercholesterolemia, and an aggravation of atherosclerosis in its presence. In the current study, plasma Hcy levels were increased by intravenous injections of Hcy into balloon-injured rabbits fed VCDD (VCDD+Hcy) in the absence of hypercholesterolemia. While this treatment did not lead to thickening of aortic wall, intravenous injections of Hcy into rabbits fed VCDD led to massive accumulation of VLDL-triglycerides as well as significant impairment of vascular reactivity of the aorta compared to VCDD alone. In the aorta intravenous Hcy injections into VCDD-fed rabbits led to fragmentation of aortic elastin, accumulation of elastin-specific electron-dense inclusions, collagen disorganization, lipid degradation, and autophagolysosome formation. Furthermore, rabbits from the VCDD+Hcy group exhibited a massive decrease of total protein methylated arginine in blood cells and decreased creatine in blood cells, serum and liver compared to rabbits from the VCDD group. Altogether, we conclude that Hcy contributes to atherogenic transformation of the aorta not only in the presence but also in the absence of hypercholesterolemia.

The serum metabolomic profiles of atrial fibrillation patients treated with direct oral anticoagulants or vitamin K antagonists

AIMS

Long-term oral anticoagulation is the primary therapy for preventing ischemic stroke in patients with atrial fibrillation (AF). Different types of oral anticoagulant drugs can have specific effects on the metabolism of patients. Here we characterize, for the first time, the serum metabolomic and lipoproteomic profiles of AF patients treated with anticoagulants: vitamin K antagonists (VKAs) or direct oral anticoagulants (DOACs).

MATERIALS AND METHODS

Serum samples of 167 AF patients (median age 78 years, 62 % males, 70 % on DOACs treatment) were analyzed via high resolution 1H nuclear magnetic resonance (NMR) spectroscopy. Data on 25 metabolites and 112 lipoprotein-related fractions were quantified and analyzed with multivariate and univariate statistical approaches.

KEY FINDINGS

Our data provide evidence that patients treated with VKAs and DOACs present significant differences in their profiles: lower levels of alanine and lactate (odds ratio: 1.72 and 1.84), free cholesterol VLDL-4 subfraction (OR: 1.75), triglycerides LDL-1 subfraction (OR: 1.80) and 4 IDL cholesterol fractions (ORs ∼ 1.80), as well as higher levels of HDL cholesterol (OR: 0.48), apolipoprotein A1 (OR: 0.42) and 7 HDL cholesterol fractions/subfractions (ORs: 0.40-0.51) are characteristic of serum profile of patients on DOACs' therapy.

SIGNIFICANCE

Our results support the usefulness of NMR-based metabolomics for the description of the effects of oral anticoagulants on AF patient circulating metabolites and lipoproteins. The higher serum levels of HDL cholesterol observed in patients on DOACs could contribute to explaining their reduced cardiovascular risk, suggesting the need of further studies in this direction to fully understand possible clinical implications.

Blending Samples to Increase Accuracy and Precision of 1H NMR Urine Metabolomics

Urine is an equally attractive biofluid for metabolomics analysis, as it is a challenging matrix analytically. Accurate urine metabolite concentration estimates by Nuclear Magnetic Resonance (NMR) are hampered by pH and ionic strength differences between samples, resulting in large peak shift variability. Here we show that calculating the spectra of original samples from mixtures of samples using linear algebra reduces the shift problems and makes various error estimates possible. Since the use of two-dimensional (2D) NMR to confirm metabolite annotations is effectively impossible to employ on every sample of large sample sets, stabilization of metabolite peak positions increases the confidence in identifying metabolites, avoiding the pitfall of oranges-to-apples comparisons.

Metabolomic Profiling of Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy for Predicting Disease-Free and Overall Survival

Breast cancer (BC) remains a significant global health concern, with neoadjuvant chemotherapy (NACT) offering preoperative benefits like tumor downstaging and treatment response assessment. However, identifying factors influencing post-NACT treatment response and survival outcomes is challenging. Metabolomic approaches offer promising insights into understanding these outcomes. This study analyzed the serum of 80 BC patients before and after NACT, followed for up to five years, correlating with disease-free survival (DFS) and overall survival (OS). Using untargeted nuclear magnetic resonance (NMR) spectroscopy and a novel statistical model that avoids collinearity issues, we identified metabolic changes associated with survival outcomes. Four metabolites (histidine, lactate, serine, and taurine) were significantly associated with DFS. We developed a metabolite-related survival score (MRSS) from these metabolites, stratifying patients into low- and high-risk relapse groups, independent of classical prognostic factors. High-risk patients had a hazard ratio (HR) for DFS of 3.42 (95% CI 1.51-7.74; p = 0.003) after adjustment for disease stage and age. A similar trend was observed for OS (HR of 3.34, 95% CI 1.64-6.80; p < 0.001). Multivariate Cox proportional hazards analysis confirmed the independent prognostic value of the MRSS. Our findings suggest the potential of metabolomic data, alongside traditional markers, in guiding personalized treatment decisions and risk stratification in BC patients undergoing NACT. This study provides a methodological framework for leveraging metabolomics in survival analyses.

Using neural networks to obtain NMR spectra of both small and macromolecules from blood samples in a single experiment

Metabolomics plays a crucial role in understanding metabolic processes within biological systems. Using specific pulse sequences, NMR-based metabolomics detects small and macromolecular metabolites that are altered in blood samples. Here we proposed a method called spectral editing neural network, which can effectively edit and separate the spectral signals of small and macromolecules in 1H NMR spectra of serum and plasma based on the linewidth of the peaks. We applied the model to process the 1H NMR spectra of plasma and serum. The extracted small and macromolecular spectra were then compared with experimentally obtained relaxation-edited and diffusion-edited spectra. Correlation analysis demonstrated the quantitative capability of the model in the extracted small molecule signals from 1H NMR spectra. The principal component analysis showed that the spectra extracted by the model and those obtained by NMR spectral editing methods reveal similar group information, demonstrating the effectiveness of the model in signal extraction.

Metabolic Reprogramming Is an Initial Step in Pancreatic Carcinogenesis That Can Be Targeted to Inhibit Acinar-to-Ductal Metaplasia

Metabolic reprogramming is a hallmark of cancer and is crucial for cancer progression, making it an attractive therapeutic target. Understanding the role of metabolic reprogramming in cancer initiation could help identify prevention strategies. To address this, we investigated metabolism during acinar-to-ductal metaplasia (ADM), the first step of pancreatic carcinogenesis. Glycolytic markers were elevated in ADM lesions compared with normal tissue from human samples. Comprehensive metabolic assessment in three mouse models with pancreas-specific activation of KRAS, PI3K, or MEK1 using Seahorse measurements, nuclear magnetic resonance metabolome analysis, mass spectrometry, isotope tracing, and RNA sequencing analysis revealed a switch from oxidative phosphorylation to glycolysis in ADM. Blocking the metabolic switch attenuated ADM formation. Furthermore, mitochondrial metabolism was required for de novo synthesis of serine and glutathione (GSH) but not for ATP production. MYC mediated the increase in GSH intermediates in ADM, and inhibition of GSH synthesis suppressed ADM development. This study thus identifies metabolic changes and vulnerabilities in the early stages of pancreatic carcinogenesis. Significance: Metabolic reprogramming from oxidative phosphorylation to glycolysis mediated by MYC plays a crucial role in the development of pancreatic cancer, revealing a mechanism driving tumorigenesis and potential therapeutic targets. See related commentary by Storz, p. 2225.

Recent Advancement in NMR Based Plant Metabolomics: Techniques, Tools, and Analytical Approaches

Plant metabolomics, a rapidly advancing field within plant biology, is dedicated to comprehensively exploring the intricate array of small molecules in plant systems. This entails precisely gathering comprehensive chemical data, detecting numerous metabolites, and ensuring accurate molecular identification. Nuclear magnetic resonance (NMR) spectroscopy, with its detailed chemical insights, is crucial in obtaining metabolite profiles. Its widespread application spans various research disciplines, aiding in comprehending chemical reactions, kinetics, and molecule characterization. Biotechnological advancements have further expanded NMR's utility in metabolomics, particularly in identifying disease biomarkers across diverse fields such as agriculture, medicine, and pharmacology. This review covers the stages of NMR-based metabolomics, including historical aspects and limitations, with sample preparation, data acquisition, spectral processing, analysis, and their application parts.

Machine Learning-Driven SERS Nanoendoscopy and Optophysiology

A frontier of analytical sciences is centered on the continuous measurement of molecules in or near cells, tissues, or organs, within the biological context in situ, where the molecular-level information is indicative of health status, therapeutic efficacy, and fundamental biochemical function of the host. Following the completion of the Human Genome Project, current research aims to link genes to functions of an organism and investigate how the environment modulates functional properties of organisms. New analytical methods have been developed to detect chemical changes with high spatial and temporal resolution, including minimally invasive surface-enhanced Raman scattering (SERS) nanofibers using the principles of endoscopy (SERS nanoendoscopy) or optical physiology (SERS optophysiology). Given the large spectral data sets generated from these experiments, SERS nanoendoscopy and optophysiology benefit from advances in data science and machine learning to extract chemical information from complex vibrational spectra measured by SERS. This review highlights new opportunities for intracellular, extracellular, and in vivo chemical measurements arising from the combination of SERS nanosensing and machine learning.

Targeted 1-H-NMR wine analyses revealed specific metabolomic signatures of yeast populations belonging to the Saccharomyces genus

This study aimed to explore the non-volatile metabolomic variability of a large panel of strains (44) belonging to the Saccharomyces cerevisiae and Saccharomyces uvarum species in the context of the wine alcoholic fermentation. For the S. cerevisiae strains flor, fruit and wine strains isolated from different anthropic niches were compared. This phenotypic survey was achieved with a special focus on acidity management by using natural grape juices showing opposite level of acidity. A 1H NMR based metabolomics approach was developed for quantifying fifteen wine metabolites that showed important quantitative variability within the strains. Thanks to the robustness of the assay and the low amount of sample required, this tool is relevant for the analysis of the metabolomic profile of numerous wines. The S. cerevisiae and S. uvarum species displayed significant differences for malic, succinic, and pyruvic acids, as well as for glycerol and 2,3-butanediol production. As expected, S. uvarum showed weaker fermentation fitness but interesting acidifying properties. The three groups of S. cerevisiae strains showed different metabolic profiles mostly related to their production and consumption of organic acids. More specifically, flor yeast consumed more malic acid and produced more acetic acid than the other S. cerevisiae strains which was never reported before. These features might be linked to the ability of flor yeasts to shift their metabolism during wine oxidation.

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

NMR Metabolite Profiling for the Characterization of Vessalico Garlic Ecotype and Bioactivity against Xanthomonas campestris pv. campestris

The Italian garlic ecotype "Vessalico" possesses distinct characteristics compared to its French parent cultivars Messidor and Messidrôme, used for sowing, as well as other ecotypes in neighboring regions. However, due to the lack of a standardized seed supply method and cultivation protocol among farmers in the Vessalico area, a need to identify garlic products that align with the Vessalico ecotype arises. In this study, an NMR-based approach followed by multivariate analysis to analyze the chemical composition of Vessalico garlic sourced from 17 different farms, along with its two French parent cultivars, was employed. Self-organizing maps allowed to identify a homogeneous subset of representative samples of the Vessalico ecotype. Through the OPLS-DA model, the most discriminant metabolites based on values of VIP (Variable Influence on Projections) were selected. Among them, S-allylcysteine emerged as a potential marker for distinguishing the Vessalico garlic from the French parent cultivars by NMR screening. Additionally, to promote sustainable agricultural practices, the potential of Vessalico garlic extracts and its main components as agrochemicals against Xanthomonas campestris pv. campestris, responsible for black rot disease, was explored. The crude extract exhibited a MIC of 125 μg/mL, and allicin demonstrated the highest activity among the tested compounds (MIC value of 31.25 μg/mL).

Twenty Years of 1H NMR Plant Metabolomics: A Way Forward toward Assessment of Plant Metabolites for Constitutive and Inducible Defenses to Biotic Stress

Metabolomics has become an important tool in elucidating the complex relationship between a plant genotype and phenotype. For over 20 years, nuclear magnetic resonance (NMR) spectroscopy has been known for its robustness, quantitative capabilities, simplicity, and cost-efficiency. 1H NMR is the method of choice for analyzing a broad range of relatively abundant metabolites, which can be used for both capturing the plant chemical profile at one point in time and understanding the pathways that underpin plant defense. This systematic Review explores how 1H NMR-based plant metabolomics has contributed to understanding the role of various compounds in plant responses to biotic stress, focusing on both primary and secondary metabolites. It clarifies the challenges and advantages of using 1H NMR in plant metabolomics, interprets common trends observed, and suggests guidelines for method development and establishing standard procedures.

Integrated Metabolomics and Proteomics of Symptomatic and Early Presymptomatic States of Colitis

Colitis has a multifactorial pathogenesis with a strong cross-talk among microbiota, hypoxia, and tissue metabolism. Here, we aimed to characterize the molecular signature of the disease in symptomatic and presymptomatic stages of the inflammatory process at the tissue and fecal level. The study is based on two different murine models for colitis, and HR-MAS NMR on "intact" colon tissues and LC-MS/MS on colon tissue extracts were used to derive untargeted metabolomics and proteomics information, respectively. Solution NMR was used to derive metabolomic profiles of the fecal extracts. By combining metabolomic and proteomic analyses of the tissues, we found increased anaerobic glycolysis, accompanied by an altered citric acid cycle and oxidative phosphorylation in inflamed colons; these changes associate with inflammation-induced hypoxia taking place in colon tissues. Different colitis states were also characterized by significantly different metabolomic profiles of fecal extracts, attributable to both the dysbiosis characteristic of colitis as well as the dysregulated tissue metabolism. Strong and distinctive tissue and fecal metabolomic signatures can be detected before the onset of symptoms. Therefore, untargeted metabolomics of tissues and fecal extracts provides a comprehensive picture of the changes accompanying the disease onset already at preclinical stages, highlighting the diagnostic potential of global metabolomics for inflammatory diseases.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

Targeted metabolite profiling of Salvia rosmarinus Italian local ecotypes and cultivars and inhibitory activity against Pectobacterium carotovorum subsp. carotovorum

Introduction

The development of agriculture in terms of sustainability and low environmental impact is, at present, a great challenge, mainly in underdeveloped and marginal geographical areas. The Salvia rosmarinus "Eretto Liguria" ecotype is widespread in Liguria (Northwest Italy), and farmers commonly use it by for cuttings and for marketing. In the present study, this ecotype was characterized in comparison with other cultivars from the same geographical region and Campania (Southern Italy), with a view to application and registration processes for the designation of protected geographical indications. Moreover, the possibility of using the resulting biomass after removing cuttings or fronds as a source of extracts and pure compounds to be used as phytosanitary products in organic farming was evaluated. Specifically, the potential of rosemary extracts and pure compounds to prevent soft rot damage was then tested.

Methods

A targeted NMR metabolomic approach was employed, followed by multivariate analysis, to characterize the rosemary accessions. Bacterial soft rot assay and disk diffusion test were carried out to evaluate the activity of extracts and isolated compounds against Pectobacterium carotovorum subsp. carotovorum. Enzymatic assay was performed to measure the in vitro inhibition of the pectinase activity produced by the selected pathogen. Molecular docking simulations were used to explore the possible interaction of the selected compounds with the pectinase enzymes.

Results and Discussion

The targeted metabolomic analysis highlighted those different geographical locations can influence the composition and abundance of bioactive metabolites in rosemary extracts. At the same time, genetic factors are important when a single geographical area is considered. Self-organizing maps (SOMs) showed that the accessions of "Eretto Liguria" appeared well characterized when compared to the others and had a good content in specialized metabolites, particularly carnosic acid. Soft rotting Enterobacteriaceae belonging to the Pectobacterium genus represent a serious problem in potato culture. Even though rosemary methanolic extracts showed a low antibacterial activity against a strain of Pectobacterium carotovorum subsp. carotovorum in the disk diffusion test, they showed ability in reducing the soft rot damage induced by the bacterium on potato tissue. 7-O-methylrosmanol, carnosol and isorosmanol appeared to be the most active components. In silico studies indicated that these abietane diterpenoids may interact with P. carotovorum subsp. carotovorum pectate lyase 1 and endo-polygalacturonase, thus highlighting these rosemary components as starting points for the development of agents able to prevent soft rot progression.

Depicting the molecular features of suicidal behavior: a review from an "omics" perspective

Background Suicide is one of the leading global causes of death. Behavior patterns from suicide ideation to completion are complex, involving multiple risk factors. Advances in technologies and large-scale bioinformatic tools are changing how we approach biomedical problems. The "omics" field may provide new knowledge about suicidal behavior to improve identification of relevant biological pathways associated with suicidal behavior. Methods We reviewed transcriptomic, proteomic, and metabolomic studies conducted in blood and post-mortem brains from individuals who experienced suicide or suicidal behavior. Omics data were combined using systems biology in silico, aiming at identifying major biological mechanisms and key molecules associated with suicide. Results Post-mortem samples of suicide completers indicate major dysregulations in pathways associated with glial cells (astrocytes and microglia), neurotransmission (GABAergic and glutamatergic systems), neuroplasticity and cell survivor, immune responses and energy homeostasis. In the periphery, studies found alterations in molecules involved in immune responses, polyamines, lipid transport, energy homeostasis, and amino and nucleic acid metabolism. Limitations We included only exploratory, non-hypothesis-driven studies; most studies only included one brain region and whole tissue analysis, and focused on suicide completers who were white males with almost none confounding factors. Conclusions We can highlight the importance of synaptic function, especially the balance between the inhibitory and excitatory synapses, and mechanisms associated with neuroplasticity, common pathways associated with psychiatric disorders. However, some of the pathways highlighted in this review, such as transcriptional factors associated with RNA splicing, formation of cortical connections, and gliogenesis, point to mechanisms that still need to be explored.

Review on NMR as a tool to analyse natural products extract directly: Molecular structure elucidation and biological activity analysis

INTRODUCTION

Natural products, the small organic molecules produced by plants, microbes and invertebrates, often present in the form of a mixture, this leads to the structural characterisation of natural extracts often requiring time-consuming multistep purification procedures. Nuclear magnetic resonance (NMR) technology is traditionally utilised as a tool for the structural elucidation of pure compounds. Contemporarily, an up-to-date trend in the application of NMR in natural product research is shifting to the direct NMR analysis of crude mixtures, to obtain molecular structure and biological activity information without performing cumbersome separation.

OBJECTIVE

To review works of literature on the evolution, principle and progress of NMR technologies for analysing mixtures, we focus on the successful application of NMR technologies in direct analysis of natural product extracts.

METHODOLOGY

Based on our research experience, academic tracking and extensive literature search, which involved, but not limited to, the use of various databases, like Web of Knowledge and PubMed. The keywords used, in various combinations, to retrieve recent literature on the successful application of NMR technologies to sheer natural product extracts, and excluded artificially natural product mixture and biofluid.

RESULTS

NMR technologies for direct natural extracts analysis, including two-dimensional J-resolved spectroscopy (2D-JRES), pure shift NMR, diffusion-ordered NMR spectroscopy (DOSY), statistical correlation spectroscopy (STOCSY), concentration-ordered NMR spectroscopy (CORDY), saturation transfer difference (STD) and water-ligand observed via gradient spectroscopy (WaterLOGSY) were illustrated.

CONCLUSIONS

By these methods, molecular structure and biological activity information will be directly obtained from NMR analysis of natural products extract, aiming to save experimental time and expenses.

Differential temporal release and lipoprotein loading in B. thetaiotaomicron bacterial extracellular vesicles

Bacterial extracellular vesicles (BEVs) contribute to stress responses, quorum sensing, biofilm formation and interspecies and interkingdom communication. However, the factors that regulate their release and heterogeneity are not well understood. We set out to investigate these factors in the common gut commensal Bacteroides thetaiotaomicron by studying BEV release throughout their growth cycle. Utilising a range of methods, we demonstrate that vesicles released at different stages of growth have significantly different composition, with early vesicles enriched in specifically released outer membrane vesicles (OMVs) containing a larger proportion of lipoproteins, while late phase BEVs primarily contain lytic vesicles with enrichment of cytoplasmic proteins. Furthermore, we demonstrate that lipoproteins containing a negatively charged signal peptide are preferentially incorporated in OMVs. We use this observation to predict all Bacteroides thetaiotaomicron OMV enriched lipoproteins and analyse their function. Overall, our findings highlight the need to understand media composition and BEV release dynamics prior to functional characterisation and define the theoretical functional capacity of Bacteroides thetaiotaomicron OMVs.

Mapping of 1 H NMR chemical shifts relationship with chemical similarities for the acceleration of metabolic profiling: Application on blood products

One-dimensional (1D) proton-nuclear magnetic resonance (1 H-NMR) spectroscopy is an established technique for the deconvolution of complex biological sample types via the identification/quantification of small molecules. It is highly reproducible and could be easily automated for small to large-scale bioanalytical, epidemiological, and in general metabolomics studies. However, chemical shift variability is a serious issue that must still be solved in order to fully automate metabolite identification. Herein, we demonstrate a strategy to increase the confidence in assignments and effectively predict the chemical shifts of various NMR signals based upon the simplest form of statistical models (i.e., linear regression). To build these models, we were guided by chemical homology in serum/plasma metabolites classes (i.e., amino acids and carboxylic acids) and similarity between chemical groups such as methyl protons. Our models, built on 940 serum samples and validated in an independent cohort of 1,052 plasma-EDTA spectra, were able to successfully predict the 1 H NMR chemical shifts of 15 metabolites within ~1.5 linewidths (Δv1/2 ) error range on average. This pilot study demonstrates the potential of developing an algorithm for the accurate assignment of 1 H NMR chemical shifts based solely on chemically defined constraints.

Exploring the Effects of Probiotic Treatment on Urinary and Serum Metabolic Profiles in Healthy Individuals

Probiotics are live microorganisms that confer health benefits when administered in adequate amounts. They are used to promote gut health and alleviate various disorders. Recently, there has been an increasing interest in the potential effects of probiotics on human physiology. In the presented study, the effects of probiotic treatment on the metabolic profiles of human urine and serum using a nuclear magnetic resonance (NMR)-based metabonomic approach were investigated. Twenty-one healthy volunteers were enrolled in the study, and they received two different dosages of probiotics for 8 weeks. During the study, urine and serum samples were collected from volunteers before and during probiotic supplementation. The results showed that probiotics had a significant impact on the urinary and serum metabolic profiles without altering their phenotypes. This study demonstrated the effects of probiotics in terms of variations of metabolite levels resulting also from the different probiotic posology. Overall, the results suggest that probiotic administration may affect both urine and serum metabolomes, although more research is needed to understand the mechanisms and clinical implications of these effects. NMR-based metabonomic analysis of biofluids is a powerful tool for monitoring host-gut microflora dynamic interaction as well as for assessing the individual response to probiotic treatment.

NMR-based metabolomics in Alzheimer's disease research: a review

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and represents the most common cause of dementia in the elderly population worldwide. Currently, there is no cure for AD, and the continuous increase in the number of susceptible individuals poses one of the most significant emerging threats to public health. However, the molecular pathways involved in the onset and progression of AD are not fully understood. This information is crucial for developing less invasive diagnostic instruments and discovering novel potential therapeutic targets. Metabolomics studies the complete ensemble of endogenous and exogenous metabolites present in biological specimens and may provide an interesting approach to identify alterations in multiple biochemical processes associated with AD onset and evolution. In this mini review, we summarize the results from metabolomic studies conducted using nuclear magnetic resonance (NMR) spectroscopy on human biological samples (blood derivatives, cerebrospinal fluid, urine, saliva, and tissues) from AD patients. We describe the metabolic alterations identified in AD patients compared to controls and to patients diagnosed with mild cognitive impairment (MCI). Moreover, we discuss the challenges and issues associated with the application of NMR-based metabolomics in the context of AD research.

1H NMR Metabolomics on Pigs' Liver Exposed to Antibiotics Administration: An Explorative Study

An untargeted Nuclear Magnetic Resonance (NMR) spectroscopy-based metabolomics approach was applied as a first attempt to explore the metabolome of pigs treated with antibiotics. The final goal was to investigate the possibility of discriminating between antibiotic-treated (TX group) and untreated pigs (CTRL group), with the further perspective of identifying the authentication tools for antibiotic-free pork supply chains. In particular, 41 samples of pig liver were subjected to a biphasic extraction to recover both the polar and the non-polar metabolites, and the 1H NMR spectroscopy analysis was performed on the two separate extracts. Unsupervised (principal component analysis) and supervised (orthogonal partial least squares discriminant analysis) multivariate statistical analysis of 1H NMR spectra data in the range 0-9 ppm provided metabolomic fingerprinting useful for the discrimination of pig livers based on the antibiotic treatment to which they were exposed. Moreover, within the signature patterns, significant discriminating metabolites were identified among carbohydrates, choline and derivatives, amino acids and some lipid-class molecules. The encouraging findings of this exploratory study showed the feasibility of the untargeted metabolomic approach as a novel strategy in the authentication framework of pork supply chains and open a new horizon for a more in-depth investigation.

HDL-Related Parameters and COVID-19 Mortality: The Importance of HDL Function

COVID-19, caused by the SARS-CoV-2 coronavirus, emerged as a global pandemic in late 2019, resulting in significant global public health challenges. The emerging evidence suggests that diminished high-density lipoprotein (HDL) cholesterol levels are associated with the severity of COVID-19, beyond inflammation and oxidative stress. Here, we used nuclear magnetic resonance spectroscopy to compare the lipoprotein and metabolic profiles of COVID-19-infected patients with non-COVID-19 pneumonia. We compared the control group and the COVID-19 group using inflammatory markers to ensure that the differences in lipoprotein levels were due to COVID-19 infection. Our analyses revealed supramolecular phospholipid composite (SPC), phenylalanine, and HDL-related parameters as key discriminators between COVID-19-positive and non-COVID-19 pneumonia patients. More specifically, the levels of HDL parameters, including apolipoprotein A-I (ApoA-I), ApoA-II, HDL cholesterol, and HDL phospholipids, were significantly different. These findings underscore the potential impact of HDL-related factors in patients with COVID-19. Significantly, among the HDL-related metrics, the cholesterol efflux capacity (CEC) displayed the strongest negative association with COVID-19 mortality. CEC is a measure of how well HDL removes cholesterol from cells, which may affect the way SARS-CoV-2 enters cells. In summary, this study validates previously established markers of COVID-19 infection and further highlights the potential significance of HDL functionality in the context of COVID-19 mortality.

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

COVID-19: A complex disease with a unique metabolic signature

Plasma of COVID-19 patients contains a strong metabolomic/lipoproteomic signature, revealed by the NMR analysis of a cohort of >500 patients sampled during various waves of COVID-19 infection, corresponding to the spread of different variants, and having different vaccination status. This composite signature highlights common traits of the SARS-CoV-2 infection. The most dysregulated molecules display concentration trends that scale with disease severity and might serve as prognostic markers for fatal events. Metabolomics evidence is then used as input data for a sex-specific multi-organ metabolic model. This reconstruction provides a comprehensive view of the impact of COVID-19 on the entire human metabolism. The human (male and female) metabolic network is strongly impacted by the disease to an extent dictated by its severity. A marked metabolic reprogramming at the level of many organs indicates an increase in the generic energetic demand of the organism following infection. Sex-specific modulation of immune response is also suggested.

Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas

Soft tissue sarcomas (STSs) are rare malignant tumors that are difficult to prognosticate using currently available instruments. Omics sciences could provide more accurate and individualized survival predictions for patients with metastatic STS. In this pilot, hypothesis-generating study, we integrated clinicopathological variables with proton nuclear magnetic resonance (1H NMR) plasma metabolomic and lipoproteomic profiles, capturing both tumor and host characteristics, to identify novel prognostic biomarkers of 2-year survival. Forty-five metastatic STS (mSTS) patients with prevalent leiomyosarcoma and liposarcoma histotypes receiving trabectedin treatment were enrolled. A score combining acetate, triglycerides low-density lipoprotein (LDL)-2, and red blood cell count was developed, and it predicts 2-year survival with optimal results in the present cohort (84.4% sensitivity, 84.6% specificity). This score is statistically significant and independent of other prognostic factors such as age, sex, tumor grading, tumor histotype, frailty status, and therapy administered. A nomogram based on these 3 biomarkers has been developed to inform the clinical use of the present findings.

Compatibility of Nucleobases Containing Pt(II) Complexes with Red Blood Cells for Possible Drug Delivery Applications

The therapeutic advantages of some platinum complexes as major anticancer chemotherapeutic agents and of nucleoside analogue-based compounds as essential antiviral/antitumor drugs are widely recognized. Red blood cells (RBCs) offer a potential new strategy for the targeted release of therapeutic agents due to their biocompatibility, which can protect loaded drugs from inactivation in the blood, thus improving biodistribution. In this study, we evaluated the feasibility of loading model nucleobase-containing Pt(II) complexes into human RBCs that were highly stabilized by four N-donors and susceptible to further modification for possible antitumor/antiviral applications. Specifically, platinum-based nucleoside derivatives [PtII(dien)(N7-Guo)]2+, [PtII(dien)(N7-dGuo)]2+, and [PtII(dien)(N7-dGTP)] (dien = diethylenetriamine; Guo = guanosine; dGuo = 2'-deoxy-guanosine; dGTP = 5'-(2'-deoxy)-guanosine-triphosphate) were investigated. These Pt(II) complexes were demonstrated to be stable species suitable for incorporation into RBCs. This result opens avenues for the possible incorporation of other metalated nucleobases analogues, with potential antitumor and/or antiviral activity, into RBCs.

Effects of Sample Dilution on Nuclear Magnetic Resonance-Derived Metabolic Profiles of Human Urine

Traditionally, a relatively big urine volume (e.g., 500 μL) is used in nuclear magnetic resonance (NMR)-based human metabolomics, which is not feasible for studies with limited/precious samples. Although urine may be diluted before conventional high-throughput metabolomics analysis, the comprehensive effect of urine dilution on metabolic profiles is unknown. Here, for the first time, we systematically investigated the effect of urine dilution on 1H NMR metabolic profiles, by evaluating signal detectability, integration, signal-to-noise ratio (SNR), chemical shift (δ) and its variation, and signal overlapping of 47 metabolites in 10 volunteers. We observed significant linear changes along with increased dilution, including decreased integration and SNR, altered δ, decreased intersample variation of δ, and increased separation between overlapped signals, e.g., lactate and threonine, β-d-glucose and an unassigned signal, and histidine and 3-methylhistidine. We further tested the 40% dilution level (i.e., employing 300 μL urine) in an epidemiological study containing 1018 pregnant women from the Tongji-Shuangliu Birth Cohort, showing acceptable detectability and chemical shift variability for most of the 47 metabolites profiled. It indicated that mild (e.g., 40%) dilution of human urine can largely preserve the high-abundance metabolites profiled, reduce intersample chemical shift variations, and increase separations of overlapped signals, which is an improvement of routine sample preparation methods in NMR-based metabolomics and is applicable for studies with limited urine volumes, including large-scale epidemiological studies.

Metabolomic Profile, Plasmatic Levels of Losartan and EXP3174, Blood Pressure Control in Hypertensive Patients and Their Correlation with COVID-19

Systemic arterial hypertension (SAH) is one of the most prevalent chronic diseases worldwide and is related to serious health complications. It has been pointed out as a major risk factor for COVID-19. This study aimed to determine the impact of COVID-19 on the metabolomic profile, the correlation with the plasmatic levels of losartan and its active metabolite (EXP3174), biochemical markers, and blood pressure (BP) control in hypertensive patients. 1H NMR metabolomic profiles of hypertensive and normotensive patients with and without previous COVID-19 diagnosis were identified. Plasmatic levels of LOS and EXP3174 were correlated with BP, biochemical markers, and the metabolomic fingerprint of the groups. Biomarkers linked to important aspects of SAH and COVID-19 were identified, such as glucose, glutamine, arginine, creatinine, alanine, choline, erythritol, homogentisate, 0-tyrosine, and 2-hydroxybutyrate. Those metabolites are indicative of metabolic alterations, kidney damage, pulmonary dysfunction, and persistent inflammation, which can be found in both diseases. Some hypertensive patients did not reach the therapeutic levels of LOS and EXP3174, while the BP control was also limited among the normotensive patients with previous COVID-19 diagnoses. Metabolomics proved to be an important tool for assessing the effectiveness of losartan pharmacotherapy and the damage caused by SAH and COVID-19 in hypertensive patients.

Towards a Precise NMR Quantification of Acute Phase Inflammation Proteins from Human Serum

Nuclear Magnetic Resonance (NMR) spectra of human serum and plasma show, besides metabolites and lipoproteins, two characteristic signals termed GlycA and B arising from the acetyl groups of glycoprotein glycans from acute phase proteins, which constitute good markers for inflammatory processes. Here, we report a comprehensive assignment of glycoprotein glycan NMR signals observed in human serum, showing that GlycA and GlycB signals originate from Neu5Ac and GlcNAc moieties from N-glycans, respectively. Diffusion-edited NMR experiments demonstrate that signal components can be associated with specific acute phase proteins. Conventionally determined concentrations of acute phase glycoproteins correlate well with distinct features in NMR spectra (R2 up to 0.9422, p-value <0.001), allowing the simultaneous quantification of several acute phase inflammation proteins. Overall, a proteo-metabolomics NMR signature of significant diagnostic potential is obtained within 10-20 min acquisition time. This is exemplified in serum samples from COVID-19 and cardiogenic shock patients showing significant changes in several acute phase proteins compared to healthy controls.