NMR-Based Metabolomics

Molecular biomarkers identification and applications in CHO bioprocessing

Biomarkers are valuable tools in clinical research where they allow to predict susceptibility to diseases, or response to specific treatments. Likewise, biomarkers can be extremely useful in the biomanufacturing of therapeutic proteins. Indeed, constraints such as short timelines and the need to find hyper-productive cells could benefit from a data-driven approach during cell line and process development. Many companies still rely on large screening capacities to develop productive cell lines, but as they reach a limit of production, there is a need to go from empirical to rationale procedures. Similarly, during bioprocessing runs, substrate consumption and metabolism wastes are commonly monitored. None of them possess the ability to predict the culture behavior in the bioreactor. Big data driven approaches are being adapted to the study of industrial mammalian cell lines, enabled by the publication of Chinese hamster and CHO genome assemblies which allowed the use of next-generation sequencing with these cells, as well as continuous proteome and metabolome annotation. However, if these different -omics technologies contributed to the characterization of CHO cells, there is a significant effort remaining to apply this knowledge to biomanufacturing methods. The correlation of a complex phenotype such as high productivity or rapid growth to the presence or expression level of a specific biomarker could save time and effort in the screening of manufacturing cell lines or culture conditions. In this review we will first discuss the different biological molecules that can be identified and quantified in cells, their detection techniques, and associated challenges. We will then review how these markers are used during the different steps of cell line and bioprocess development, and the inherent limitations of this strategy.

Metabolomic profiling of elite female soccer players: urinary biomarkers over a championship season

INTRODUCTION

In soccer, most studies evaluate metabolic profile changes in male athletes, often using data from a single match. Given the current landscape of women's soccer and the effects of biological sex on the physiological response and adaptation to exercise, more studies targeting female athletes and analyzing pre- and post-game moments throughout the season are necessary.

OBJECTIVES

To describe the metabolomics profile of female soccer athletes from an elite team in Brazil. The study observed the separation of groups in three pre- and post-game moments and identified the discriminating metabolites.

METHODS

The study included 14 female soccer athletes. Urine samples were collected and analyzed using Nuclear Magnetic Resonance in pre-game and immediate post-game moments over three national championship games. The metabolomics data were then used to generate OPLS-DA and VIP plots.

RESULTS

Forty-three metabolites were identified in the samples. OPLS-DA analyses demonstrated a progressive separation between pre-post conditions, as supported by an increasing Q2 value (0.534, 0.625, and 0.899 for games 1, 2 and 3, respectively) and the first component value (20.2% and 19.1% in games 1 and 2 vs. 29.9% in game 3). Eight out of the fifteen most discriminating metabolites appeared consistently across the three games: glycine, formate, citrate, 3-hydroxyvalerate, glycolic acid, trimethylamine, urea, and dimethylglycine.

CONCLUSION

The main difference between the three games was the increasing separation between groups throughout the championship. Since the higher VIP-scores metabolites are linked to energy and protein metabolism, this separation may be attributed several factors, one being the accumulation of fatigue.

A simplified metabolomic analysis of dried blood spots in breast cancer patients

Breast cancer (BC) is among the most commonly diagnosed cancers. Besides mammography, breast ultrasonography and the routinely monitored protein markers, the variations of small molecular metabolites in blood may be of great diagnostic value. This study aimed to quantify specific metabolite markers with potential application in BC detection. The study enrolled 50 participants, 25 BC patients and 25 healthy controls (CTRL). Dried blood spots (DBS) were utilized as biological media and were quantified via a simplified liquid chromatography tandem mass spectrometry (LC-MS/MS) method, used in expanded newborn screening. The targeted metabolomic analysis included 12 amino acids and 32 acylcarnitines. Statistical analysis revealed a significant variation of metabolic profiles between BC patients and CTRL. Among the 44 metabolites, 18 acylcarnitines and 10 amino acids remained significant after Bonferroni correction, showing increase or decrease and enabled classification of BC patients and CTRL. The well-established LC-MS/MS protocol could provide results within few minutes. Therefore, the combination of an easy-to-handle material-DBS and LC-MS/MS protocol could facilitate BC screening/diagnosis and in the next step applied to other cancer patients, as well.

Protective effects of oyster polypeptide on cyclophosphamide-induced immunosuppressed rats

BACKGROUND

Oyster polypeptide (OP) is a mixture of oligopeptides extracted from oysters through enzyme lysis, separation, and purification. It is associated with immunomodulatory effects, but the underlying mechanisms are not known. This study therefore combined proton nuclear magnetic resonance (1H-NMR) urinary metabolomics and 16S rRNA gene sequencing of the gut microbiome to determine the immunoprotective mechanisms of OP in rats subjected to cyclophosphamide-induced immunosuppression.

RESULTS

Oyster polypeptide restored the body weight and the structure of spleen and thymus in rats with cyclophosphamide-induced immunosuppression. It upregulated the levels of white blood cells (WBCs), hemoglobin (HGB), platelets (PLT), red blood cells (RBCs), immunoglobulin G (IgG), immunoglobulin M (IgM), cytokines such as interleukin‑6 (IL-6) and tumor necrosis factor-α (TNF-α), and increased the numbers of CD3+ and CD4+ T cells in the immunosuppressed rats. The 1H-NMR metabolomics results showed that OP significantly reversed the levels of ten metabolites in urine, including 2-oxoglutarate, citrate, dimethylamine, taurine, N-phenylacetylglycine, alanine, betaine, creatinine, uracil, and benzoate. The 16S rRNA gene sequencing results showed that OP restored the gut microbiome homeostasis by increasing the abundance of beneficial bacteria and reducing the abundance of pathogenic bacteria. Finally, a combination of metabolomics and microbiomics found that the metabolism of taurine and hypotaurine, and the metabolism of alanine, aspartate, and glutamate were disturbed, but these metabolic pathways were restored by OP.

CONCLUSION

This study demonstrated that OP had immunoprotective effects in rats with cyclophosphamide-induced immunosuppression by restoring key metabolic pathways and the gut microbiome homeostasis. Our findings provide a framework for further research into the immunoregulatory mechanisms of OP and its potential use in drugs and nutritional supplements. © 2024 Society of Chemical Industry.

Assessing inorganic nanoparticle toxicity through omics approaches

In the last two decades, the development of nanotechnology has resulted in inorganic nanoparticles playing crucial roles in key industries, ranging from healthcare to energy technologies. For instance, gold and silver nanoparticles are widely used in rapid COVID-19 and flu tests, titania and zinc oxide nanoparticles are commonly found in cosmetic products, and superparamagnetic iron oxide nanoparticles have been clinically exploited as contrast agents and anti-anemia medicines. As a result, human exposure to nanomaterials is continuously increasing, raising concerns about their potential adverse health effects. Historically, the study of nanoparticle toxicity has largely relied on macroscopic observations obtained in different in vitro and in vivo models, resulting in readouts such as median lethal dose, biodistribution profile, and/or histopathological assessment. In recent years, omics methodologies, including transcriptomics, epigenomics, proteomics, metabolomics, and lipidomics, are increasingly used to characterize the biological interactions of nanomaterials, providing a better and broader understanding of their impact and mechanisms of toxicity. These approaches have been able to identify important genes and gene products that mediate toxicological effects, as well as endogenous functions and pathways dysregulated by nanoparticles. Omics methods improve our understanding of nanoparticle biology, and unravel mechanistic insights into nanomedicine-based therapies. This review aims to provide a deeper understanding and new perspectives of omics approaches to characterize the toxicity and biological interactions of inorganic nanoparticles, and improve the safety of nanoparticle applications.

Connecting the dots: investigating the link between environmental, genetic, and epigenetic influences in metabolomic alterations in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) accounts for around 90% of all oral cancers and is the eighth most common cancer worldwide. Despite progress in managing OSCC, the overall prognosis remains poor, with a survival rate of around 50-60%, largely due to tumor size and recurrence. The challenges of late-stage diagnosis and limitations in current methods emphasize the urgent need for less invasive techniques to enable early detection and treatment, crucial for improving outcomes in this aggressive form of oral cancer. Research is currently aimed at unraveling tumor-specific metabolite profiles to identify candidate biomarkers as well as discover underlying pathways involved in the onset and progression of cancer that could be used as new targets for diagnostic and therapeutic purposes. Metabolomics is an advanced technological approach to identify metabolites in different sample types (biological fluids and tissues). Since OSCC promotes metabolic reprogramming influenced by a combination of genetic predisposition and environmental factors, including tobacco and alcohol consumption, and viral infections, the identification of distinct metabolites through screening may aid in the diagnosis of this condition. Moreover, studies have shown the use of metabolites during the catalysis of epigenetic modification, indicating a link between epigenetics and metabolism. In this review, we will focus on the link between environmental, genetic, and epigenetic influences in metabolomic alterations in OSCC. In addition, we will discuss therapeutic targets of tumor metabolism, which may prevent oral tumor growth, metastasis, and drug resistance.

1H NMR spectroscopic characterisation of HepG2 cells as a model metabolic system for toxicology studies

The immortalised human hepatocellular HepG2 cell line is commonly used for toxicology studies as an alternative to animal testing due to its characteristic liver-distinctive functions. However, little is known about the baseline metabolic changes within these cells upon toxin exposure. We have applied 1H Nuclear Magnetic Resonance (NMR) spectroscopy to characterise the biochemical composition of HepG2 cells at baseline and post-exposure to hydrogen peroxide (H2O2). Metabolic profiles of live cells, cell extracts, and their spent media supernatants were obtained using 1H high-resolution magic angle spinning (HR-MAS) NMR and 1H NMR spectroscopic techniques. Orthogonal partial least squares discriminant analysis (O-PLS-DA) was used to characterise the metabolites that differed between the baseline and H2O2 treated groups. The results showed that H2O2 caused alterations to 10 metabolites, including acetate, glutamate, lipids, phosphocholine, and creatine in the live cells; 25 metabolites, including acetate, alanine, adenosine diphosphate (ADP), aspartate, citrate, creatine, glucose, glutamine, glutathione, and lactate in the cell extracts, and 22 metabolites, including acetate, alanine, formate, glucose, pyruvate, phenylalanine, threonine, tryptophan, tyrosine, and valine in the cell supernatants. At least 10 biochemical pathways associated with these metabolites were disrupted upon toxin exposure, including those involved in energy, lipid, and amino acid metabolism. Our findings illustrate the ability of NMR-based metabolic profiling of immortalised human cells to detect metabolic effects on central metabolism due to toxin exposure. The established data sets will enable more subtle biochemical changes in the HepG2 model cell system to be identified in future toxicity testing.

Exploring microbiota-gut-brain axis biomarkers linked to autism spectrum disorder in prenatally chlorpyrifos-exposed Fmr1 knock-out and wild-type male rats

Fmr1 (fragile X messenger ribonucleoprotein 1)-knockout (KO) rats, modeling the human Fragile X Syndrome (FXS), are of particular interest for exploring the ASD-like phenotype in preclinical studies. Gestational exposure to chlorpyrifos (CPF) has been associated with ASD diagnosis in humans and ASD-like behaviors in rodents and linked to the microbiota-gut-brain axis. In this study, we have used both Fmr1-KO and wild-type male rats (F2 generation) at postnatal days (PND) 7 and 40 obtained after F1 pregnant females were randomly exposed to 1 mg/kg/mL/day of CPF or vehicle. A nuclear magnetic resonance (NMR) metabolomics approach together with gene expression profiles of these F2 generation rats were employed to analyze different brain regions (such as prefrontal cortex, hippocampus, and cerebellum), whole large intestine (at PND7) and gut content (PND40). The statistical comparison of each matrix spectral profile unveiled tissue-specific metabolic fingerprints. Significant variations in some biomarker levels were detected among brain tissues of different genotypes, including taurine, myo-inositol, and 3-hydroxybutyric acid, and exposure to CPF induced distinct metabolic alterations, particularly in serine and myo-inositol. Additionally, this study provides a set of metabolites associated with gastrointestinal dysfunction in ASD, encompassing several amino acids, choline-derived compounds, bile acids, and sterol molecules. In terms of gene expression, genotype and gestational exposure to CPF had only minimal effects on decarboxylase 2 (gad2) and cholinergic receptor muscarinic 2 (chrm2) genes.

Exploration of TCM syndrome types of the material basis and risk prediction of Wilson disease liver fibrosis based on 1H NMR metabolomics

Wilson disease (WD) is an autosomal recessive disorder characterized by abnormal copper metabolism. The accumulation of copper in the liver can progress to liver fibrosis and, ultimately, cirrhosis, which is a primary cause of death in WD patients. Metabonomic technology offers an effective approach to investigate the traditional Chinese medicine (TCM) syndrome types of WD-related liver fibrosis by monitoring the alterations in small molecule metabolites within the body. In this study, we employed 1H-Nuclear Magnetic Resonance (1H NMR) metabonomics to assess the metabolic profiles associated with five TCM syndrome types of WD-related liver fibrosis and analyzed the diagnostic and predictive capabilities of various metabolites. The study found a variety of metabolites, each with varying levels of diagnostic and predictive capabilities. Furthermore, the discerned differential metabolic pathways were primarily associated with various pathways involving carbohydrate metabolism, amino acid metabolism, and lipid metabolism. This study has identified various characteristic metabolic markers and pathways associated with different TCM syndromes of liver fibrosis in WD, providing a substantial foundation for investigating the mechanisms underlying these TCM syndromes.

Nuclear magnetic resonance spectroscopy reveals biomarkers of stroke recovery in a mouse model of obesity-associated type 2 diabetes

Obesity and Type 2 diabetes (T2D) are known to exacerbate cerebral injury caused by stroke. Metabolomics can provide signatures of metabolic disease, and now we explored whether the analysis of plasma metabolites carries biomarkers of how obesity and T2D impact post-stroke recovery. Male mice were fed a high-fat diet (HFD) for 10 months leading to development of obesity with T2D or a standard diet (non-diabetic mice). Then, mice were subjected to either transient middle cerebral artery occlusion (tMCAO) or sham surgery and allowed to recover on standard diet for 2 months before serum samples were collected. Nuclear magnetic resonance (NMR) spectroscopy of serum samples was used to investigate metabolite signals and metabolic pathways that were associated with tMCAO recovery in either T2D or non-diabetic mice. Overall, after post-stroke recovery there were different serum metabolite profiles in T2D and non-diabetic mice. In non-diabetic mice, which show full neurological recovery after stroke, we observed a reduction of isovalerate, and an increase of kynurenate, uridine monophosphate, gluconate and N6-acetyllysine in tMCAO relative to sham mice. In contrast, in mice with T2D, which show impaired stroke recovery, there was a reduction of N,N-dimethylglycine, succinate and proline, and an increase of 2-oxocaproate in serum of tMCAO versus sham mice. Given the inability of T2D mice to recover from stroke, in contrast with non-diabetic mice, we propose that these specific metabolite changes following tMCAO might be used as biomarkers of neurophysiological recovery after stroke in T2D.

Effects of diets on risks of cancer and the mediating role of metabolites

Research on the association between dietary adherence and cancer risk is limited, particularly concerning overall cancer risk and its underlying mechanisms. Using the UK Biobank data, we prospectively investigate the associations between adherence to a Mediterranean diet (MedDiet) or a Mediterranean-Dietary Approaches to Stop Hypertension Diet Intervention for Neurodegenerative Delay diet (MINDDiet) and the risk of overall and 22 specific cancers, as well as the mediating effects of metabolites. Here we show significant negative associations of MedDiet and MINDDiet adherence with overall cancer risk. These associations remain robust across 14 and 13 specific cancers, respectively. Then, a sequential analysis, incorporating Cox regression, elastic net and gradient boost models, identify 10 metabolites associated with overall cancer risk. Mediation results indicate that these metabolites play a crucial role in the association between adherence to a MedDiet or a MINDDiet and cancer risk, independently and cumulatively. These findings deepen our understanding of the intricate connections between diet, metabolites, and cancer development.

Flower Species Ingredient Verification Using Orthogonal Molecular Methods

Flowers are gaining considerable interest among consumers as ingredients in food, beverages, cosmetics, and natural health products. The supply chain trades in multiple forms of botanicals, including fresh whole flowers, which are easier to identify than dried flowers or flowers processed as powdered or liquid extracts. There is a gap in the scientific methods available for the verification of flower species ingredients traded in the supply chains of multiple markets. The objective of this paper is to develop methods for flower species ingredient verification using two orthogonal methods. More specifically, the objectives of this study employed both (1) DNA-based molecular diagnostic methods and (2) NMR metabolite fingerprint methods in the identification of 23 common flower species ingredients. NMR data analysis reveals considerable information on the variation in metabolites present in different flower species, including color variants within species. This study provides a comprehensive comparison of two orthogonal methods for verifying flower species ingredient supply chains to ensure the highest quality products. By thoroughly analyzing the benefits and limitations of each approach, this research offers valuable insights to support quality assurance and improve consumer confidence.

Tear metabolomics for the diagnosis of primary open-angle glaucoma

Primary Open-Angle Glaucoma (POAG) is the most prevalent glaucoma type, and the leading cause of irreversible visual impairment and blindness worldwide. Identification of early POAG biomarkers is of enormous value, as there is not an effective treatment for the glaucomatous optic nerve degeneration (OND). In this pilot study, a metabolomic analysis, by using proton (1H) nuclear magnetic resonance (NMR) spectroscopy was conducted in tears, in order to determine the changes of specific metabolites in the initial glaucoma eyes and to discover potential diagnostic biomarkers. A classification model, based on the metabolomic fingerprint in tears was generated as a non-invasive tool to support the preclinical and clinical POAG diagnosis. 1H NMR spectra were acquired from 30 tear samples corresponding to the POAG group (n = 11) and the control group (n = 19). Data were analysed by multivariate statistics (partial least squares-discriminant analysis: PLS-DA) to determine a model capable of differentiating between groups. The whole data set was split into calibration (65%)/validation (35%), to test the performance and the ability for glaucoma discrimination. The calculated PLS-DA model showed an area under the curve (AUC) of 1, as well as a sensitivity of 100% and a specificity of 83.3% to distinguish POAG group versus control group tear data. This model included 11 metabolites, potential biomarkers of the disease. When comparing the study groups, a decrease in the tear concentration of phenylalanine, phenylacetate, leucine, n-acetylated compounds, formic acid, and uridine, was found in the POAG group. Moreover, an increase in the tear concentration of taurine, glycine, urea, glucose, and unsaturated fatty acids was observed in the POAG group. These results highlight the potential of tear metabolomics by 1H NMR spectroscopy as a non-invasive approach to support early POAG diagnosis and in order to prevent visual loss.

Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies

Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.

Uracil as a biomarker for spatial pyrimidine metabolism in the development of gingivobuccal oral squamous cell carcinoma

No biomarker has yet been identified that allows accurate diagnosis and prognosis of oral cancers. In this study, we investigated the presence of key metabolites in oral cancer using proton nuclear magnetic resonance (NMR) spectroscopy to identify metabolic biomarkers of gingivobuccal oral squamous cell carcinoma (GB-OSCC). NMR spectroscopy revealed that uracil was expressed in 83.09% of tumor tissues and pyrimidine metabolism was active in GB-OSCC; these results correlated well with immunohistochemistry (IHC) and RNA sequencing data. Based on further gene and protein analyses, we proposed a pathway for the production of uracil in GB-OSCC tissues. Uridinetriphosphate (UTP) is hydrolyzed to uridine diphosphate (UDP) by CD39 in the tumor microenvironment (TME). We hypothesized that UDP enters the cell with the help of the UDP-specific P2Y6 receptor for further processing by ENTPD4/5 to produce uracil. As the ATP reserves diminish, the weakened immune cells in the TME utilize pyrimidine metabolism as fuel for antitumor activity, and the same mechanism is hijacked by the tumor cells to promote their survival. Correspondingly, the differential expression of ENTPD4 and ENTPD5 in immune and tumor cells, respectively, indicatedtheir involvement in disease progression. Furthermore, higher uracil levels were detected in patients with lymph node metastasis, indicating that metastatic potential is increased in the presence of uracil. The presence of uracil and/or expression patterns of intermediate molecules in purine and pyrimidine pathways, such asCD39, CD73, and P2Y6 receptors together with ENTPD4 and ENTPD5, hold promise as biomarker(s) for oral cancer diagnosis and prognosis.

Comparison of Various Extraction Approaches for Optimized Preparation of Intracellular Metabolites from Human Mesenchymal Stem Cells and Fibroblasts for NMR-Based Study

Metabolomics has proven to be a sensitive tool for monitoring biochemical processes in cell culture. It enables multi-analysis, clarifying the correlation between numerous metabolic pathways. Together with other analysis, it thus provides a global view of a cell's physiological state. A comprehensive analysis of molecular changes is also required in the case of mesenchymal stem cells (MSCs), which currently represent an essential portion of cells used in regenerative medicine. Reproducibility and correct measurement are closely connected to careful metabolite extraction, and sample preparation is always a critical point. Our study aimed to compare the efficiencies of four harvesting and six extraction methods. Several organic reagents (methanol, ethanol, acetonitrile, methanol-chloroform, MTBE) and harvesting approaches (trypsinization vs. scraping) were tested. We used untargeted nuclear magnetic resonance spectroscopy (NMR) to determine the most efficient method for the extraction of metabolites from human adherent cells, specifically human dermal fibroblasts adult (HDFa) and dental pulp stem cells (DPSCs). A comprehensive dataset of 29 identified and quantified metabolites were determined to possess statistically significant differences in the abundances of several metabolites when the cells were detached mechanically to organic solvent compared to when applying enzymes mainly in the classes of amino acids and peptides for both types of cells. Direct scraping to organic solvent is a method that yields higher abundances of determined metabolites. Extraction with the use of different polar reagents, 50% and 80% methanol, or acetonitrile, mostly showed the same quality. For both HDFa and DPSC cells, the MTBE method, methanol-chloroform, and 80% ethanol extractions showed higher extraction efficiency for the most identified and quantified metabolites Thus, preparation procedures provided a cell sample processing protocol that focuses on maximizing extraction yield. Our approach may be useful for large-scale comparative metabolomic studies of human mesenchymal stem cell samples.

Metabolomic and proteomic applications to exercise biomedicine

Objectives

'OMICs encapsulates study of scaled data acquisition, at the levels of DNA, RNA, protein, and metabolite species. The broad objectives of OMICs in biomedical exercise research are multifarious, but commonly relate to biomarker development and understanding features of exercise adaptation in health, ageing and metabolic diseases.

Methods

This field is one of exponential technical (i.e., depth of feature coverage) and scientific (i.e., in health, metabolic conditions and ageing, multi-OMICs) progress adopting targeted and untargeted approaches.

Results

Key findings in exercise biomedicine have led to the identification of OMIC features linking to heritability or adaptive responses to exercise e.g., the forging of GWAS/proteome/metabolome links to cardiovascular fitness and metabolic health adaptations. The recent addition of stable isotope tracing to proteomics ('dynamic proteomics') and metabolomics ('fluxomics') represents the next phase of state-of-the-art in 'OMICS.

Conclusions

These methods overcome limitations associated with point-in-time 'OMICs and can be achieved using substrate-specific tracers or deuterium oxide (D2O), depending on the question; these methods could help identify how individual protein turnover and metabolite flux may explain exercise responses. We contend application of these methods will shed new light in translational exercise biomedicine.

Efficient Quantification of Milk Metabolites from 1H NMR Spectra Using the Signature Mapping (SigMa) Approach: Chemical Shift Library Development for Cows' Milk and Colostrum

Cow milk contains essential nutrients for humans, and its bulk composition is usually analyzed using Fourier transform infrared spectroscopy. The higher sensitivity of nuclear magnetic resonance (NMR) spectroscopy can augment the extractible qualitative and quantitative information from milk to nearly 60 compounds, enabling us to monitor the health of cows and milk quality. Proton (1H) NMR spectroscopy produces complex spectra that require expert knowledge for identifying and quantifying metabolites. Therefore, an efficient and reproducible methodology is required to transform complex milk 1H NMR spectra into annotated and quantified milk metabolome data. In this study, standard operating procedures for screening the milk metabolome using 1H NMR spectra are developed. A chemical shift library of 63 milk metabolites was established and implemented in the open-access Signature Mapping (SigMa) software. SigMa is a spectral analysis tool that transforms 1H NMR spectra into a quantitative metabolite table. The applicability of the proposed methodology to whole milk, skim milk, and ultrafiltered milk is demonstrated, and the method is tested on ultrafiltered colostrum samples from dairy cows (n = 88) to evaluate whether metabolic changes in colostrum may reflect the metabolic status of cows.

Proton Nuclear Magnetic Resonance (1H NMR) Metabolomics Study in Serum, Urine, and Cystic Fluid for Differentiating Fertility and Staging of Intra-abdominal Hydatid Cyst in Adults

Background

Cystic echinococcosis (CE) is a parasitic zoonosis caused by the tapeworm Echinococcus granulosus. Over the past few years, a lot of research has been done on liver illnesses using metabolomics techniques to identify biomarkers which could identify the diseases in its early stages. The present study was done to explore biomarkers in serum, urine, and cystic fluid which would help in differentiating, staging, and assessing fertility of intra-abdominal hydatid cyst by using proton nuclear magnetic resonance (1H NMR) metabolomics.

Materials and methods

In the study, 28 subjects (16 cases and 12 controls) were enrolled. Staging of hydatid cysts was performed using ultrasonography. In patients complying with case and control definition, blood, urine, and cystic fluid were collected for complete blood count, urine culture, Echinococcus IgG enzyme-linked immunosorbent assay (ELISA), and metabolomic analysis. The 17, 15, and 11 metabolites in serum, urine, and cystic fluid samples were quantified, respectively, to differentiate between case and control group.

Results

In this study, we observed that there was a significant downregulation of succinate metabolite in urine samples of cases, down-regulation of five metabolites (isoleucine, valine, histidine, tyrosine and formate) and upregulation of alanine in cystic fluid of cases.

Conclusion

Current study demonstrates that metabolomics can be used non-invasively for rapid diagnosis of CE. This is one of the very few studies, which used 1H NMR spectroscopy, to analyze the profile of metabolites in serum, urine, and cystic fluid in cases of CE and controls.

How to cite this article

Raj N, Pandey A, Roy R, et al. Proton Nuclear Magnetic Resonance (1H NMR) Metabolomics Study in Serum, Urine, and Cystic Fluid for Differentiating Fertility and Staging of Intra-abdominal Hydatid Cyst in Adults. Euroasian J Hepato-Gastroenterol 2024;14(1):30-34.

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

INTRODUCTION

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Serum Metabolomics of Retinoblastoma: Assessing the Differential Serum Metabolic Signatures of Unilateral and Bilateral Patients

Retinoblastoma (Rb) is the most common pediatric eye cancer. To identify the biomarkers for early diagnosis and monitoring the progression of Rb in patients, mapping of the alterations in their metabolic profiles is essential. The present study aims at exploring the metabolic disparity in serum from Rb patients and controls using NMR-based metabolomics. A total of 72 metabolites, including carbohydrates, amino acids, and organic acids, were quantified in serum samples from 24 Rb patients and 26 controls. Distinct clusters of Rb patients and controls were obtained using the partial least-squares discriminant analysis (PLS-DA) model. Further, univariate and multivariate analyses of unilateral and bilateral Rb patients with respect to their age-matched controls depicted their distinct metabolic fingerprints. Metabolites including 2-phosphoglycerate, 4-aminobutyrate, proline, O-phosphocholine, O-phosphoethanolamine, and Sn-glycero-3-phosphocholine (Sn-GPC) showed significant perturbation in both unilateral and bilateral Rb patients. However, metabolic differences among the bilateral Rb cases were more pronounced than those in unilateral Rb cases with respect to controls. In addition to major discriminatory metabolites for Rb, unilateral and bilateral Rb cases showed specific metabolic changes, which might be the result of their differential genetic/somatic mutational backgrounds. This further suggests that the aberrant metabolic perturbation in bilateral patients signifies the severity of the disease in Rb patients. The present study demonstrated that identified serum metabolites have potential to serve as a noninvasive method for detection of Rb, discriminate bilateral from unilateral Rb patients, and aid in better understanding of the RB tumor biology.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Monitoring live mitochondrial metabolism in real-time using NMR spectroscopy

Investigation of mitochondrial metabolism is gaining increased interest owing to the growing recognition of the role of mitochondria in health and numerous diseases. Studies of isolated mitochondria promise novel insights into the metabolism devoid of confounding effects from other cellular organelles such as cytoplasm. This study describes the isolation of mitochondria from mouse skeletal myoblast cells (C2C12) and the investigation of live mitochondrial metabolism in real-time using isotope tracer-based NMR spectroscopy. [3-13 C1 ]pyruvate was used as the substrate to monitor the dynamic changes of the downstream metabolites in mitochondria. The results demonstrate an intriguing phenomenon, in which lactate is produced from pyruvate inside the mitochondria and the results were confirmed by treating mitochondria with an inhibitor of mitochondrial pyruvate carrier (UK5099). Lactate is associated with health and numerous diseases including cancer and, to date, it is known to occur only in the cytoplasm. The insight that lactate is also produced inside mitochondria opens avenues for exploring new pathways of lactate metabolism. Further, experiments performed using inhibitors of the mitochondrial respiratory chain, FCCP and rotenone, show that [2-13 C1 ]acetyl coenzyme A, which is produced from [3-13 C1 ]pyruvate and acts as a primary substrate for the tricarboxylic acid cycle in mitochondria, exhibits a remarkable sensitivity to the inhibitors. These results offer a direct approach to visualize mitochondrial respiration through altered levels of the associated metabolites.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Exploring Human Metabolome after Wine Intake-A Review

Wine has a rich history dating back to 2200 BC, originally recognized for its medicinal properties. Today, with the aid of advanced technologies like metabolomics and sophisticated analytical techniques, we have gained remarkable insights into the molecular-level changes induced by wine consumption in the human organism. This review embarks on a comprehensive exploration of the alterations in human metabolome associated with wine consumption. A great number of 51 studies from the last 25 years were reviewed; these studies systematically investigated shifts in metabolic profiles within blood, urine, and feces samples, encompassing both short-term and long-term studies of the consumption of wine and wine derivatives. Significant metabolic alterations were observed in a wide variety of metabolites belonging to different compound classes, such as phenolic compounds, lipids, organic acids, and amino acids, among others. Within these classes, both endogenous metabolites as well as diet-related metabolites that exhibited up-regulation or down-regulation following wine consumption were included. The up-regulation of short-chain fatty acids and the down-regulation of sphingomyelins after wine intake, as well as the up-regulation of gut microbial fermentation metabolites like vanillic and syringic acid are some of the most important findings reported in the reviewed literature. Our results confirm the intact passage of certain wine compounds, such as tartaric acid and other wine acids, to the human organism. In an era where the health effects of wine consumption are of growing interest, this review offers a holistic perspective on the metabolic underpinnings of this centuries-old tradition.

Recent Advances and Perspectives in Relation to the Metabolomics-Based Study of Diabetic Retinopathy

Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes, is a major cause of acquired blindness in adults. Currently, a clinical diagnosis of DR primarily relies on fundus fluorescein angiography, with a limited availability of effective biomarkers. Metabolomics, a discipline dedicated to scrutinizing the response of various metabolites within living organisms, has shown noteworthy advancements in uncovering metabolic disorders and identifying key metabolites associated with DR in recent years. Consequently, this review aims to present the latest advancements in metabolomics techniques and comprehensively discuss the principal metabolic outcomes derived from analyzing blood, vitreous humor, aqueous humor, urine, and fecal samples.

Metabolite Alterations in Autoimmune Diseases: A Systematic Review of Metabolomics Studies

Autoimmune diseases, characterized by the immune system's loss of self-tolerance, lack definitive diagnostic tests, necessitating the search for reliable biomarkers. This systematic review aims to identify common metabolite changes across multiple autoimmune diseases. Following PRISMA guidelines, we conducted a systematic literature review by searching MEDLINE, ScienceDirect, Google Scholar, PubMed, and Scopus (Elsevier) using keywords "Metabolomics", "Autoimmune diseases", and "Metabolic changes". Articles published in English up to March 2023 were included without a specific start date filter. Among 257 studies searched, 88 full-text articles met the inclusion criteria. The included articles were categorized based on analyzed biological fluids: 33 on serum, 21 on plasma, 15 on feces, 7 on urine, and 12 on other biological fluids. Each study presented different metabolites with indications of up-regulation or down-regulation when available. The current study's findings suggest that amino acid metabolism may serve as a diagnostic biomarker for autoimmune diseases, particularly in systemic lupus erythematosus (SLE), multiple sclerosis (MS), and Crohn's disease (CD). While other metabolic alterations were reported, it implies that autoimmune disorders trigger multi-metabolite changes rather than singular alterations. These shifts could be consequential outcomes of autoimmune disorders, representing a more complex interplay. Further studies are needed to validate the metabolomics findings associated with autoimmune diseases.

Applications of Solution NMR Spectroscopy in Quality Assessment and Authentication of Bovine Milk

Nuclear magnetic resonance (NMR) spectroscopy is emerging as a promising technique for the analysis of bovine milk, primarily due to its non-destructive nature, minimal sample preparation requirements, and comprehensive approach to untargeted milk analysis. These inherent strengths of NMR make it a formidable complementary tool to mass spectrometry-based techniques in milk metabolomic studies. This review aims to provide a comprehensive overview of the applications of NMR techniques in the quality assessment and authentication of bovine milk. It will focus on the experimental setup and data processing techniques that contribute to achieving accurate and highly reproducible results. The review will also highlight key studies that have utilized commonly used NMR methodologies in milk analysis, covering a wide range of application fields. These applications include determining milk animal species and feeding regimes, as well as assessing milk nutritional quality and authenticity. By providing an overview of the diverse applications of NMR in milk analysis, this review aims to demonstrate the versatility and significance of NMR spectroscopy as an invaluable tool for milk and dairy metabolomics research and hence, for assessing the quality and authenticity of bovine milk.

Metabolite Profiling in a Diet-Induced Obesity Mouse Model and Individuals with Diabetes: A Combined Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy Study

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and ¹H NMR spectroscopy improves the discrimination of metabolic disease development. We induced metabolic syndrome in male mice using a high-fat diet (HFD) exposure and performed LC-MS and NMR spectroscopy on plasma samples collected after 1 and 8 weeks of dietary intervention. In an orthogonal projection to latent structures (OPLS) analysis, we observed that combining MS and NMR was stronger than each analytical method alone at determining effects of both HFD feeding and time-on-diet. We then tested our metabolomics approach on plasma from 56 individuals from the Malmö Diet and Cancer Study (MDCS) cohort. All metabolic pathways impacted by HFD feeding in mice were confirmed to be affected by diabetes in the MDCS cohort, and most prominent HFD-induced metabolite concentration changes in mice were also associated with metabolic syndrome parameters in humans. The main drivers of metabolic disease discrimination emanating from the present study included plasma levels of xanthine, hippurate, 2-hydroxyisovalerate, S-adenosylhomocysteine and dimethylguanidino valeric acid. In conclusion, our combined NMR-MS approach provided a snapshot of metabolic imbalances in humans and a mouse model, which was improved over employment of each analytical method alone.

Metabolomics and its applications in assisted reproductive technology

Metabolomics, an emerging omics technology developed in the post-gene age, is an important part of systems biology. It interprets the pathophysiological state of the subject by quantitatively describing the dynamic changes of metabolites through analytical methods, mainly mass spectrometry (MS) and nuclear magnetic resonance (NMR). Assisted reproductive technology (ART) is a method used to manipulate sperm, oocytes, and embryos to achieve conception. Recently, several studies have reported that metabolomics methods can be used to measure metabolites in ART samples; these metabolites can be used to evaluate the quality of gametes and embryos. This article reviews the progress of research on metabolomics and the application of this technology in the field of ART, thus providing a reference for research and development directions in the future.

Saponins of North Atlantic Sea Cucumber: Chemistry, Health Benefits, and Future Prospectives

Frondosides are the major saponins (triterpene glycosides) of the North Atlantic sea cucumber (Cucumaria frondosa). Frondosides possess amphiphilic characteristics due to the presence of various hydrophilic sugar moieties and hydrophobic genin (sapogenin). Saponins are abundant in holothurians, including in sea cucumbers that are widely distributed across the northern part of the Atlantic Ocean. Over 300 triterpene glycosides have been isolated, identified, and categorized from many species of sea cucumbers. Furthermore, specific saponins from sea cucumbers are broadly classified on the basis of the fron-dosides that have been widely studied. Recent studies have shown that frondoside-containing extracts from C. frondosa exhibit anticancer, anti-obesity, anti-hyperuricemic, anticoagulant, antioxidant, antimicrobial, antiangiogenic, antithrombotic, anti-inflammatory, antitumor, and immunomodulatory activities. However, the exact mechanism(s) of action of biological activities of frondosides is not clearly understood. The function of some frondosides as chemical defense molecules need to be understood. Therefore, this review discusses the different frondosides of C. frondosa and their potential therapeutic activities in relation to the postulated mechanism(s) of action. In addition, recent advances in emerging extraction techniques of frondosides and other saponins and future perspectives are discussed.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Metabolomics for biomarker discovery in schistosomiasis: A systematic scoping review

Background

Metabolomic based approaches are essential tools in the discovery of unique biomarkers for infectious diseases via high-throughput global assessment of metabolites and metabolite pathway dysregulation. This in-turn allows the development of diagnostic tools and provision of therapeutics. In this review, we aimed to give an overview of metabolite biomarkers and metabolic pathway alterations during Schistosoma haematobium and Schistosoma mansoni infections.

Methods

We conducted the review by systematically searching electronic databases and grey literature to identify relevant metabolomics studies on schistosomiasis. Arksey and O’Malley methodology for conducting systematic scoping reviews was applied. A narrative summary of results was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping review guidelines.

Results

Twelve articles included in the review identified 127 metabolites, whose concentrations were considerably altered during S. mansoni and S. haematobium infections. The metabolites were assigned to metabolic pathways involved in energy (34.6%), gut microbial (11.0%), amino acid (25.2%), nucleic acids (6.3%), immune proteins (8.7%) hormones (2.4%) and structural proteins/lipids (11.8%). Energy related metabolic pathways were the most affected during schistosome infections with metabolites such as succinate, citrate, aconitate and fumarate of the tricarbocylic acid cycle being significantly altered in organ, serum and plasma samples. Amino acid metabolism was also impacted during schistosome infections as phenylacetylglycine, alanine, taurine, 2-oxoisocaproate and 2-oxoisovalerate emerged as potent biomarkers. Elevated structural proteins such as actin, collagen and keratin concentrations were identified as biomarkers of liver fibrosis, a common pathological feature in chronic schistosomiasis infections. Hippurate was a major metabolite biomarker in the gut microbial related pathway.

Conclusions

The analysis of the literature revealed that energy related metabolic pathways are considerably altered during S. mansoni and S. haematobium infections. Therefore, their metabolites may provide biomarkers for diagnosis and prognosis in addition to providing therapeutics for parasitic infections. This scoping review has identified a need to replicate more schistosomiasis metabolomic studies in humans to complement animal-model based studies.

Skin Cancer Metabolic Profile Assessed by Different Analytical Platforms

Skin cancer, including malignant melanoma (MM) and keratinocyte carcinoma (KC), historically named non-melanoma skin cancers (NMSC), represents the most common type of cancer among the white skin population. Despite decades of clinical research, the incidence rate of melanoma is increasing globally. Therefore, a better understanding of disease pathogenesis and resistance mechanisms is considered vital to accomplish early diagnosis and satisfactory control. The "Omics" field has recently gained attention, as it can help in identifying and exploring metabolites and metabolic pathways that assist cancer cells in proliferation, which can be further utilized to improve the diagnosis and treatment of skin cancer. Although skin tissues contain diverse metabolic enzymes, it remains challenging to fully characterize these metabolites. Metabolomics is a powerful omics technique that allows us to measure and compare a vast array of metabolites in a biological sample. This technology enables us to study the dermal metabolic effects and get a clear explanation of the pathogenesis of skin diseases. The purpose of this literature review is to illustrate how metabolomics technology can be used to evaluate the metabolic profile of human skin cancer, using a variety of analytical platforms including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and nuclear magnetic resonance (NMR). Data collection has not been based on any analytical method.

Chemical shift variations in common metabolites

The reliability and robustness of metabolite assignments in 1H NMR is complicated by numerous factors including variations in temperature, pH, buffer choice, ionic strength, and mixture composition that led to peak overlap and spectral crowding. As sample conditions fluctuate, peak drift and line broadening further complicate peak deconvolution and subsequent chemical assignment. We present a collection of 1D 1H NMR spectra of 54 common metabolites at varied pH (6.0 to 8.0 in 0.5 step increments) and temperature (290 K to 308 K) to quantify chemical shift variability to facilitate automated metabolite assignments. Our results illustrate the fundamental challenges with accurately assigning NMR peaks under varied environmental conditions prevalent in complex mixtures. Phosphorylated metabolites showed a larger variation in chemical shifts due to pH, whereas; amino acids showed a higher variation due to temperature. Mixtures of phosphorous compounds showed a consistently poor reliability in achieving an accurate assignment. Phosphorylated cholines, amino acids, and glycerols yielded a 40 % false negative rate for 7 out of 9 mixture conditions. Amino acids had a false negative rate of 57 % at 298 K and pH 8. Our results demonstrate that the automated assignments of complex biofluid mixtures require an expert to intervene to confirm the accuracy of metabolite assignments. Our analysis also indicates the need for reference databases to include spectra under a variety of conditions that includes mixtures and a range of pH and temperature to improve the accuracy and reproducibility of metabolite assignments.

On the Relevance of Soft Tissue Sarcomas Metabolic Landscape Mapping

Soft tissue sarcomas (STS) prognosis is disappointing, with current treatment strategies being based on a "fit for all" principle and not taking distinct sarcoma subtypes specificities and genetic/metabolic differences into consideration. The paucity of precision therapies in STS reflects the shortage of studies that seek to decipher the sarcomagenesis mechanisms. There is an urge to improve STS diagnosis precision, refine STS classification criteria, and increase the capability of identifying STS prognostic biomarkers. Single-omics and multi-omics studies may play a key role on decodifying sarcomagenesis. Metabolomics provides a singular insight, either as a single-omics approach or as part of a multi-omics strategy, into the metabolic adaptations that support sarcomagenesis. Although STS metabolome is scarcely characterized, untargeted and targeted metabolomics approaches employing different data acquisition methods such as mass spectrometry (MS), MS imaging, and nuclear magnetic resonance (NMR) spectroscopy provided important information, warranting further studies. New chromatographic, MS, NMR-based, and flow cytometry-based methods will offer opportunities to therapeutically target metabolic pathways and to monitorize the response to such metabolic targeting therapies. Here we provide a comprehensive review of STS omics applications, comprising a detailed analysis of studies focused on the metabolic landscape of these tumors.

Metabolomics: A New Approach in the Evaluation of Effects in Human Beings and Wildlife Associated with Environmental Exposition to POPs

Human beings and wild organisms are exposed daily to a broad range of environmental stressors. Among them are the persistent organic pollutants that can trigger adverse effects on these organisms due to their toxicity properties. There is evidence that metabolomics can be used to identify biomarkers of effect by altering the profiles of endogenous metabolites in biological fluids or tissues. This approach is relatively new and has been used in vitro studies mainly. Therefore, this review addresses those that have used metabolomics as a key tool to identify metabolites associated with environmental exposure to POPs in wildlife and human populations and that can be used as biomarkers of effect. The published results suggest that the metabolic pathways that produce energy, fatty acids, and amino acids are commonly affected by POPs. Furthermore, these pathways can be promoters of additional effects. In the future, metabolomics combined with other omics will improve understanding of the origin, development, and progression of the effects caused by environmental exposure.

Metabolomic Study on Iohexol-Induced Nephrotoxicity in Rats Based on NMR and LC-MS Analyses

Iohexol, the raw material of nonionic X-ray computed tomography (X-CT) contrast medium, is usually injected into the vein before CT angiography diagnosis. It is used for angiography, urography, and lymphography. With the advantages of low contrast density and good tolerance, it is currently one of the most popular contrast media. However, the renal toxicity of iohexol seriously affects its safety use. Therefore, it is of great importance to identify new potential diagnostic biomarkers and therapeutic targets in the process of contrast medium-induced acute kidney injury (CI-AKI) in order to safely use iohexol in clinical practice. In this study, in order to understand the metabolic mechanism of CI-AKI, ultra-high-performance liquid chromatography/quadrupole-Orbitrap-mass spectrometry and ¹H NMR-based metabolomic techniques were utilized to study the metabolic spectra of kidney, plasma, and urine from CI-AKI rats, and a total of 30 metabolites that were closely related to kidney injury were screened out, which were mainly related to 9 metabolic pathways. The results further indicated that iohexol might intensify kidney dysfunction in vivo by disrupting the metabolic pathways in the body, especially through blocking energy metabolism, amino acid metabolism, and promoting inflammatory reactions.

Metabolite discovery: Biochemistry's scientific driver

Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase and the foundation of metabolism's rebirth in the 21^st century; yet, characterizing newly observed metabolites has been an enduring obstacle. Crystallography and NMR spectroscopy have been of extraordinary importance, although their applicability in resolving metabolism's fine structure has been restricted by their intrinsic requirement of sufficient and sufficiently pure materials. Mass spectrometry has been a key technology, especially when coupled with high-performance separation technologies and emerging informatic and database solutions. Even more so, the collective of artificial intelligence technologies are rapidly evolving to help solve the metabolite characterization conundrum. This perspective describes this challenge, how it was historically addressed, and how metabolomics is evolving to address it today and in the future.