NMR Spectroscopy for Metabolomics Research

Probing and gauging of D-Penicillamine xenobiotics in hepatic Wilson disease patients

D-penicillamine (PA) is the primary chelator of choice to treat Wilson disease (WD). There are limitations in obtaining comprehensive data on PA metabolites in biological specimens by conventional approaches. Hence, the aim of the present was to identify the major hepatic PA metabolites and draw clear conclusions of the drug's xenobiotic in WD. Urine samples were collected from children with hepatic WD (n = 63, aged 14.8 ± 4 years) 5 h after PA administration (16.3 ± 3.8 mg/kg/day) and age-matched healthy volunteers comprised as controls (n = 30). High-resolution 800 MHz nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry was applied to reveal unambiguous appraisals of different excretory by-products of PA metabolism. Four new products comprising penicillamine disulphide (PD), penicillamine cysteine disulphide (PCD), S-methyl penicillamine (SMP), and N-acetyl penicillamine (NAP) of PA xenobiotic metabolites were identified using high-resolution NMR spectroscopy. Quantitative levels of PCD and SMP were approximately three-fold higher than those of PD and NAP, respectively. High-resolution NMR identifies the major PA metabolites with certainty. Reduction, sulfation, and methylation are the predominant pathways of PA metabolism. There is a potential application for assessing therapeutic monitoring of chelation in hepatic WD.

Metabolomic Applications in Gut Microbiota-Host Interactions in Human Diseases

The human gut microbiota, consisting of trillions of microorganisms, encodes diverse metabolic pathways that impact numerous aspects of host physiology. One key way in which gut bacteria interact with the host is through the production of small metabolites. Several of these microbiota-dependent metabolites, such as short-chain fatty acids, have been shown to modulate host diseases. In this review, we examine how disease-associated metabolic signatures are identified using metabolomic platforms, and where metabolomics is applied in gut microbiota-disease interactions. We further explore how integration of metagenomic and metabolomic data in human studies can facilitate biomarkers discoveries in precision medicine.

Metabolic Perturbations Associated with both PFAS Exposure and Perinatal/Antenatal Depression in Pregnant Individuals: A Meet-in-the-Middle Scoping Review

PURPOSE OF REVIEW

Depression during the perinatal or antenatal period affects at least 1 in 10 women worldwide, with long term health implications for the mother and child. Concurrently, there is increasing evidence associating maternal exposure to per- and poly-fluoroalkyl substances (PFAS) to adverse pregnancy outcomes. We reviewed the body of evidence examining both the associations between PFAS exposure and perturbations in the maternal metabolome, and the associations between the maternal metabolome and perinatal/antenatal depression. Through this, we sought to explore existing evidence of the perinatal metabolome as a potential mediation pathway linking PFAS exposure and perinatal/antenatal depression.

RECENT FINDINGS

There are few studies examining the metabolomics of PFAS exposure-specifically in pregnant women-and the metabolomics of perinatal/antenatal depression, let alone studies examining both simultaneously. Of the studies reviewed (N = 11), the majority were cross sectional, based outside of the US, and conducted on largely homogenous populations. Our review identified 23 metabolic pathways in the perinatal metabolome common to both PFAS exposure and perinatal/antenatal depression. Future studies may consider findings from our review to conduct literature-derived hypothesis testing focusing on fatty acid metabolism, alanine metabolism, glutamate metabolism, and tyrosine metabolism when exploring the biochemical mechanisms conferring the risk of perinatal/antenatal depression due to PFAS exposure. We recommend that researchers also utilize heterogenous populations, longitudinal study designs, and mediation approaches to elucidate key pathways linking PFAS exposures to perinatal/antenatal depression.

The development of machine learning approaches in two-dimensional NMR data interpretation for metabolomics applications

Metabolomics has been widely applied in human diseases and environmental science to study the systematic changes of metabolites over diverse types of stimuli. NMR-based metabolomics has been widely used, but the peak overlap problems in the one-dimensional (1D) NMR spectrum could limit the accuracy of quantitative analysis for metabolomics applications. Two-dimensional (2D) NMR has been applied to solve the 1D NMR overlap problem, but the data processing is still challenging. In this study, we built an automatic approach to process the 2D NMR data for quantitative applications using machine learning approaches. Partial least square discriminant analysis (PLS-DA), artificial neural network classification (ANN-DA), gradient boosted trees classification (XGBoost-DA), and artificial deep learning neural network classification (ANNDL-DA) were applied in combination with an automatic peak selection approach. Standard mixtures, sea anemone extracts, and mouse fecal samples were tested to demonstrate the approach. Our results showed that ANN-DA and ANNDL-DA have high accuracy in selecting 2D NMR peaks (around 90%), which have a high potential application in 2D NMR-based metabolomics quantitively study, while PLS-DA and XGBoost-DA showed limitations in either data variation or overfitting. Our study built an automatic approach to applying 2D NMR data to routine quantitative analysis in metabolomics.

Enhancement of weak signals by applying a suppression method to high-intense methyl and methylene signals of lipids in NMR spectroscopy

Lipids play crucial roles in human biology, serving as energy stores, cell membranes, hormone production, and signaling molecules. Accordingly, their study under lipidomics has advanced the study of living organisms. 1-Dimensional (D) and 2D NMR methods, particularly 1D 1H and 2D 1H-1H Total Correlation Spectroscopy (TOCSY), are commonly used in lipidomics for quantification and structural identification. However, these NMR methods suffer from low sensitivity, especially in cases of low concentrated molecules such as protons attached to hydroxy, esters, aliphatic, or aromatic unsaturated carbons. Such molecules are common in complex mixtures such as dairy products and plant oils. On the other hand, lipids have highly populated fractions of methyl and methylene groups that result in intense peaks that overwhelm lower peaks and cause inhomogeneities in 2D TOCSY spectra. In this study, we applied a method of suppression to suppress these intense peaks of methyl and methylene groups to detect weaker peaks. The suppression method was investigated on samples of cheese, butter, a mixture of lipids, coconut oil, and olive oil. A significant improvement in peak sensitivity and visibility of cross-peaks was observed, leading to enhanced comparative quantification and structural identification of a greater number of lipids. Additionally, the enhanced sensitivity reduced the time required for the qualitative and comparative quantification of other lipid compounds and components. This, in turn, enables faster and more reliable structural identification and comparative quantification of a greater number of lipids. Additionally, it reduces the time required for the qualitative, and comparative quantification due to the enhancement of sensitivity.

Normalization of gene counts affects principal components-based exploratory analysis of RNA-sequencing data

Normalization of count data is an essential step of in the analysis of RNA-sequencing data. Its statistical analysis has been mostly addressed in the context of differential expression analysis, that is in the univariate setting. However, relationships between genes and samples are better explored and quantified using multivariate exploratory data analysis tools like Principal Component Analysis (PCA). In this study we investigate how normalization impacts PCA models and interpretation, considering twelve different widely used normalization methods that were applied on simulated and experimental data. Correlation patterns in the normalized data were explored using both summary statistics and Covariance Simultaneous Component Analysis. The impact of normalization on the PCA solutions was assessed by exploring the model complexity, the quality of sample clustering in the low-dimensional PCA space and gene ranking in the model fit to normalized data. PCA models upon normalization were interpreted in the context gene enrichment pathway analysis. We found that although PCA score plots are often similar independently form the normalization used, biological interpretation of the models can depend heavily on the normalization method applied.

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.

Metabolomics and anti-inflammatory activity of Commiphora madagascariensis jacq. leaves extract using in vitro and in vivo models

C. madagascariensis, an unexplored species of Burseraceae is used by local population for the management of inflammation and throat pain. The disease alleviation by this plant could be due to the presence of rich repository of active compounds with various pharmacological importances. In this study, therefore, the profiling of metabolites and isolation of active compounds of C. madagascariensis was performed. Furthermore, the ethanol, ethyl acetate extracts and a selected active compound was subjected for in vitro and in vivo anti-inflammatory activities. Metabolomic analysis identified and quantified 116 metabolites from leaves, young stem and gum-resins of C. madagascariensis (Burseraceae) followed by multivariate PCA analysis. NMR, GC-MS and HPLC were used to analyze primary and secondary metabolites. Subsequently, five main isolated compounds were identified as trimethoxy tetrahydrobenzo dioxolo isochromene (TTDI), butyl phenol, butyl propionate phenol, germacrone and β-elemenone. Amongst them, TTDI was found to be a novel compound. Hence, a process was developed to obtain the enriched fraction of TTDI in ethanol and ethyl acetate extracts of leaves. Furthermore, TTDI and extracts were subjected for their in vitro anti-inflammatory activity in LPS sensitized murine splenocytes. The results showed that TTDI and both extracts significantly suppressed the levels of pro-inflammatorycytokines (TNF-α, IFN-γ). Interestingly, the suppression of pro-inflammatory cytokines was evenmore significant by the similar concentration of TTDI when compared with colchicine. However, the level of anti-inflammatory cytokine (IL-10) was found to be unchanged. Additionally, in vivo anti-inflammatory study revealed a significant reduction in carrageenan induced paw edema by TTDI and both the extracts. In the docking study, TTDI was more active than colchicine with strong binding affinity to COX-2, PLA2, and 5β reductase. Our results highlighted that the presence of metabolites with medicinal and nutraceutical importance in C. madagascariensis, could provide opportunities for the development of a new plant-based therapeutics for inflammation.

Unveiling the impact of harvest time on Dioscorea opposita Thunb. cv. Tiegun maturity by NMR-based metabolomics and LC-MS/MS analysis

BACKGROUND

Dioscorea opposita Thunb. cv. Tiegun maturity (DM) is an important factor influencing its quality. However, there are few studies on the impact of harvest time on its maturation. In the present study, a NMR-based metabolomics approach was applied to investigate the dynamic metabolic changes of D. opposita Thunb. cv. Tiegun at six different harvest stages: stage 1 (S1), stage 2 (S2), Stage 3 (S3), stage 4 (S4), stage 5 (S5) and stage 6 (S6).

RESULTS

Principal component analysis showed distinct segregation of samples obtained from S1, S2 and S3 compared to those derived from S4, S5 and S6. Interestingly, these samples from the two periods were obtained before and after frost, indicating that frost descent might be important for DM. Eight differential metabolites responsible for good separation of different groups were identified by the principal component analysis loading plot and partial least squares-discriminant analysis. In addition, quantitative analysis of these metabolites using liquid chromatography-tandem mass spectrometry determined the effects of harvest time on these metabolite contents, two of which, sucrose and allantoin, were considered as potential biomarkers to determine DM.

CONCLUSION

The present study demonstrated that NMR-based metabolomics approach could serve as a powerful tool to identify differential metabolites during harvesting processes, also offering a fresh insight into understanding the DM and the potential mechanism of quality formation. © 2024 Society of Chemical Industry.

Metabolomics studies in common multifactorial eye disorders: a review of biomarker discovery for age-related macular degeneration, glaucoma, diabetic retinopathy and myopia

Introduction

Multifactorial Eye disorders are a significant public health concern and have a huge impact on quality of life. The pathophysiological mechanisms underlying these eye disorders were not completely understood since functional and low-throughput biological tests were used. By identifying biomarkers linked to eye disorders, metabolomics enables early identification, tracking of the course of the disease, and personalized treatment.

Methods

The electronic databases of PubMed, Scopus, PsycINFO, and Web of Science were searched for research related to Age-Related macular degeneration (AMD), glaucoma, myopia, and diabetic retinopathy (DR). The search was conducted in August 2023. The number of cases and controls, the study's design, the analytical methods used, and the results of the metabolomics analysis were all extracted. Using the QUADOMICS tool, the quality of the studies included was evaluated, and metabolic pathways were examined for distinct metabolic profiles. We used MetaboAnalyst 5.0 to undertake pathway analysis of differential metabolites.

Results

Metabolomics studies included in this review consisted of 36 human studies (5 Age-related macular degeneration, 10 Glaucoma, 13 Diabetic retinopathy, and 8 Myopia). The most networked metabolites in AMD include glycine and adenosine monophosphate, while methionine, lysine, alanine, glyoxylic acid, and cysteine were identified in glaucoma. Furthermore, in myopia, glycerol, glutamic acid, pyruvic acid, glycine, cysteine, and oxoglutaric acid constituted significant metabolites, while glycerol, glutamic acid, lysine, citric acid, alanine, and serotonin are highly networked metabolites in cases of diabetic retinopathy. The common top metabolic pathways significantly enriched and associated with AMD, glaucoma, DR, and myopia were arginine and proline metabolism, methionine metabolism, glycine and serine metabolism, urea cycle metabolism, and purine metabolism.

Conclusion

This review recapitulates potential metabolic biomarkers, networks and pathways in AMD, glaucoma, DR, and myopia, providing new clues to elucidate disease mechanisms and therapeutic targets. The emergence of advanced metabolomics techniques has significantly enhanced the capability of metabolic profiling and provides novel perspectives on the metabolism and underlying pathogenesis of these multifactorial eye conditions. The advancement of metabolomics is anticipated to foster a deeper comprehension of disease etiology, facilitate the identification of novel therapeutic targets, and usher in an era of personalized medicine in eye research.

A high-resolution 13C NMR approach for profiling fatty acid unsaturation in lipid extracts and in live C. elegans

Unsaturated fatty acids (UFA) play a crucial role in central cellular processes in animals, including membrane function, development, and disease. Disruptions in UFA homeostasis can contribute to the onset of metabolic, cardiovascular, and neurodegenerative disorders. Consequently, there is a high demand for analytical techniques to study lipid compositions in live cells and multicellular organisms. Conventional analysis of UFA compositions in cells, tissues and organisms involves solvent extraction procedures coupled with analytical techniques such as gas chromatography,mass spectrometry (MS) and/or nuclear magnetic resonance (NMR) spectroscopy. As a non-destructive and non-targeted technique, NMR spectroscopy is uniquely capable of characterizing the chemical profiling of living cells and multicellular organisms. Here we use NMR spectroscopy to analyze C. elegans, enabling the determination of their lipid compositions and fatty acid unsaturation levels both in cell-free lipid extracts and in vivo. The NMR spectra of lipid extracts from wild-type and fat-3 mutant C. elegans strains revealed notable differences due to the absence of Δ-6 fatty acid desaturase activity, including the lack of arachidonic and eicosapentaenoic acyl chains. Uniform 13C-isotope labeling and high-resolution 2D solution-state NMR of live worms confirmed these findings, indicating that the signals originated from fast-tumbling lipid molecules within lipid droplets. Overall, this strategy permits the analysis of lipid storage in intact worms and has enough resolution and sensitivity to identify differences between wild type and mutant animals with impaired fatty acid desaturation. Our results establish methodological benchmarks for future investigations of fatty acid regulation in live C. elegans using NMR.

The Application of Metabolomics in Hyperlipidemia: Insights into Biomarker Discovery and Treatment Efficacy Assessment

Hyperlipidemia is a lipid metabolism disorder that refers to increased levels of total triglycerides (TGs), cholesterol (TC), and low-density lipoprotein-cholesterol (LDL-C) and decreased levels of high-density lipoprotein-cholesterol (HDL-C). It is a major public health issue with increased prevalence and incidence worldwide. The ability to identify individuals at risk of this disorder before symptoms manifest will facilitate timely intervention and management to avert potential complications. This can be achieved by employing metabolomics as an early detection method for the diagnostic biomarkers of hyperlipidemia. Metabolomics is an analytical approach used to detect and quantify metabolites. This provides the ability to explain the metabolic processes involved in the development and progression of certain diseases. In recent years, interest in the use of metabolomics to identify disease biomarkers has increased, and several biomarkers have been discovered, such as docosahexaenoic acid, glycocholic acid, citric acid, betaine, and carnitine. This review discusses the primary metabolic alterations in the context of hyperlipidemia. Furthermore, we provide an overview of recent studies on the application of metabolomics to the assessment of the efficacy of traditional herbal products and common lipid-lowering medications.

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.

Exploring the complementarity of fast multipulse and multidimensional NMR methods for metabolomics: a chemical ecology case study

This study investigates the potential and complementarity of high-throughput multipulse and multidimensional NMR methods for metabolomics. Through a chemical ecology case study, three methods are investigated, offering a continuum of methods with complementary features in terms of resolution, sensitivity and experiment time. Ultrafast 2D COSY, adiabatic INEPT and SYMAPS HSQC are shown to provide a very good classification ability, comparable to the reference 1D 1H NMR method. Moreover, a detailed analysis of discriminant buckets upon supervised statistical analysis shows that all methods are highly complementary, since they are able to highlight discriminant signals that could not be detected by 1D 1H NMR. In particular, fast 2D methods appear very efficient to discriminate signals located in highly crowded regions of the 1H spectrum. Overall, the combination of these recent methods within a single NMR metabolomics workflow allows to maximize the accessible metabolic information, and also raises exciting challenges in terms of NMR data analysis for chemical ecology.

Multiscale metabolomics techniques: Insights into neuroscience research

The field of metabolomics examines the overall composition and dynamic patterns of metabolites in living organisms. The primary methods used in metabolomics include liquid chromatography (LC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) analysis. These methods enable the identification and examination of metabolite types and contents within organisms, as well as modifications to metabolic pathways and their connection to the emergence of diseases. Research in metabolomics has extensive value in basic and applied sciences. The field of metabolomics is growing quickly, with the majority of studies concentrating on biomedicine, particularly early disease diagnosis, therapeutic management of human diseases, and mechanistic knowledge of biochemical processes. Multiscale metabolomics is an approach that integrates metabolomics techniques at various scales, including the holistic, tissue, cellular, and organelle scales, to enable more thorough and in-depth studies of metabolic processes in organisms. Multiscale metabolomics can be combined with methods from systems biology and bioinformatics. In recent years, multiscale metabolomics approaches have become increasingly important in neuroscience research due to the nervous system's high metabolic demands. Multiscale metabolomics can offer novel concepts and approaches for the diagnosis, treatment, and development of medication for neurological illnesses in addition to a more thorough understanding of brain metabolism and nervous system function. In this review, we summarize the use of multiscale metabolomics techniques in neuroscience, address the promise and constraints of these techniques, and provide an overview of the metabolome and its applications in neuroscience.

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.

Exploring the Chemical Space of the Exposome: How Far Have We Gone?

Around two-thirds of chronic human disease can not be explained by genetics alone. The Lancet Commission on Pollution and Health estimates that 16% of global premature deaths are linked to pollution. Additionally, it is now thought that humankind has surpassed the safe planetary operating space for introducing human-made chemicals into the Earth System. Direct and indirect exposure to a myriad of chemicals, known and unknown, poses a significant threat to biodiversity and human health, from vaccine efficacy to the rise of antimicrobial resistance as well as autoimmune diseases and mental health disorders. The exposome chemical space remains largely uncharted due to the sheer number of possible chemical structures, estimated at over 1060 unique forms. Conventional methods have cataloged only a fraction of the exposome, overlooking transformation products and often yielding uncertain results. In this Perspective, we have reviewed the latest efforts in mapping the exposome chemical space and its subspaces. We also provide our view on how the integration of data-driven approaches might be able to bridge the identified gaps.

Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies.

METHODS

We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN.

RESULTS

Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care.

CONCLUSION

Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Keratin from Animal By-Products: Structure, Characterization, Extraction and Application-A Review

Keratin is a structural fibrous protein and the core constituent of animal by-products from livestock such as wool, feathers, hooves, horns, and pig bristles. This natural polymer is also the main component of human hair and is present at an important percentage in human and animal skin. Significant amounts of keratin-rich animal tissues are discarded worldwide each year, ca. 12 M tons, and the share used for keratin extraction and added-value applications is still very low. An important stream of new potential raw materials, represented by animal by-products and human hair, is thus being lost, while a large-scale valorization could contribute to a circular bioeconomy and to the reduction in the environmental fingerprint of those tissues. Fortunately, scientific research has made much important progress in the last 10-15 years in the better understanding of the complex keratin architecture and its variability among different animal tissues, in the development of tailored extraction processes, and in the screening of new potential applications. Hence, this review aims at a discussion of the recent findings in the characterization of keratin and keratin-rich animal by-product structures, as well as in keratin recovery by conventional and emerging techniques and advances in valorization in several fields.

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.

Metabolic profiling reveals circulating biomarkers associated with incident and prevalent Parkinson's disease

The metabolic profile predating the onset of Parkinson's disease (PD) remains unclear. We aim to investigate the metabolites associated with incident and prevalent PD and their predictive values in the UK Biobank participants with metabolomics and genetic data at the baseline. A panel of 249 metabolites was quantified using a nuclear magnetic resonance analytical platform. PD was ascertained by self-reported history, hospital admission records and death registers. Cox proportional hazard models and logistic regression models were used to investigate the associations between metabolites and incident and prevalent PD, respectively. Area under receiver operating characteristics curves (AUC) were used to estimate the predictive values of models for future PD. Among 109,790 participants without PD at the baseline, 639 (0.58%) individuals developed PD after one year from the baseline during a median follow-up period of 12.2 years. Sixty-eight metabolites were associated with incident PD at nominal significance (P < 0.05), spanning lipids, lipid constituent of lipoprotein subclasses and ratios of lipid constituents. After multiple testing corrections (P < 9 × 10-4), polyunsaturated fatty acids (PUFA) and omega-6 fatty acids remained significantly associated with incident PD, and PUFA was shared by incident and prevalent PD. Additionally, 14 metabolites were exclusively associated with prevalent PD, including amino acids, fatty acids, several lipoprotein subclasses and ratios of lipids. Adding these metabolites to the conventional risk factors yielded a comparable predictive performance to the risk-factor-based model (AUC = 0.766 vs AUC = 0.768, P = 0.145). Our findings suggested metabolic profiles provided additional knowledge to understand different pathways related to PD before and after its onset.

Fermentation Analytical Technology (FAT): Monitoring industrial E. coli fermentations using absolute quantitative 1H NMR spectroscopy

BACKGROUND

To perform fast, reproducible, and absolute quantitative measurements in an automated manner has become of paramount importance when monitoring industrial processes, including fermentations. Due to its numerous advantages - including its inherent quantitative nature - Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy provides an ideal tool for the time-resolved monitoring of fermentations. However, analytical conditions, including non-automated sample preparation and long relaxation times (T1) of some metabolites, can significantly lengthen the experimental time and make implementation in an industrial set up unfeasible.

RESULTS

We present a high throughput method based on Standard Operating Procedures (SOPs) and 1H NMR, which lays the foundation for what we call Fermentation Analytical Technology (FAT). Our method was developed for the accurate absolute quantification of metabolites produced during Escherichia coli industrial fermentations. The method includes: (1) a stopped flow system for non-invasive sample collection followed by sample quenching, (2) automatic robot-assisted sample preparation, (3) fast 1H NMR measurements, (4) metabolites quantification using multivariate curve resolution (MCR), and (5) metabolites absolute quantitation using a novel correction factor (k) to compensate for the short recycle delay (D1) employed in the 1H NMR measurements. The quantification performance was tested using two sample types: buffer solutions of chemical standards and real fermentation samples. Five metabolites - glucose, acetate, alanine, phenylalanine and betaine - were quantified. Absolute quantitation ranged between 0.64 and 3.40 mM in pure buffer, and 0.71-7.76 mM in real samples.

SIGNIFICANCE

The proposed method is generic and can be straight forward implemented to other types of fermentations, such as lactic acid, ethanol and acetic acid fermentations. It provides a high throughput automated solution for monitoring fermentation processes and for quality control through absolute quantification of key metabolites in fermentation broth. It can be easily implemented in an at-line industrial setting, facilitating the optimization of the manufacturing process towards higher yields and more efficient and sustainable use of resources.

NMR-based metabolomic investigation of dogs with acute flaccid paralysis due to tick paralysis

BACKGROUND

Acute flaccid paralysis (AFP) is a complex clinical syndrome with various aetiologies. If untreated, AFP may lead to death due to failure of respiratory muscles. Tick paralysis, which is a noninfectious neurologic syndrome of AFP, occurs following tick attachment, engorgement, and injection of tick saliva toxins. There is no specific diagnostic test for tick paralysis, and mortality increases as definitive diagnosis is delayed. Although metabolomic investigation of tick saliva was conducted, there is a lack of research on metabolomic evaluation of hosts affected by tick paralysis.

OBJECTIVES

Thus, the aim of this study is to investigate metabolomic changes in serum samples of dogs with tick paralysis due to Rhipicephalus sanguineus using NMR-based metabolomics and to identify potential diagnostic/prognostic markers.

MATERIALS AND METHODS

Forty dogs infested with R. sanguineus, with clinical findings compatible with AFP and with a confirmed tick paralysis diagnosis ex juvantibus, constituted the Paralysis Group. Ten healthy dogs, which were admitted either for vaccination and/or check-up purposes, constituted the Control Group. After the confirmation tick paralysis, medical history, vaccination and nutritional status, body surface area and estimated tick numbers of all the dogs were noted. Physical examination included body temperature, heart and respiratory rate, capillary refill time evaluation and Modified Glasgow Coma Scale calculation. Serum samples were extracted from venous blood samples of all the dogs and were prepared for NMR analysis, and NMR-based metabolomics identification and quantification were performed.

RESULTS

NMR-based serum metabolomics of the present study revealed distinct up/down-regulated expressions, presenting a promising avenue. Moreover, it was observed that energy metabolism and especially liver functions were impaired in dogs with tick paralysis, and not only the respiratory system but also the kidneys were affected.

CONCLUSION

It was concluded that the present approach may help to better understand the pathological mechanisms developing in cases of AFP due to tick paralysis.

Perturbations in mitochondrial metabolism associated with defective cardiolipin biosynthesis: An in-organello real-time NMR study

Mitochondria are central to cellular metabolism; hence, their dysfunction contributes to a wide array of human diseases including cancer, cardiopathy, neurodegeneration, and heritable pathologies such as Barth syndrome. Cardiolipin, the signature phospholipid of the mitochondrion promotes proper cristae morphology, bioenergetic functions, and directly affects metabolic reactions carried out in mitochondrial membranes. To match tissue-specific metabolic demands, cardiolipin typically undergoes an acyl tail remodeling process with the final step carried out by the phospholipid-lysophospholipid transacylase tafazzin. Mutations in the tafazzin gene are the primary cause of Barth syndrome. Here, we investigated how defects in cardiolipin biosynthesis and remodeling impact metabolic flux through the tricarboxylic acid cycle and associated pathways in yeast. Nuclear magnetic resonance was used to monitor in real-time the metabolic fate of 13C3-pyruvate in isolated mitochondria from three isogenic yeast strains. We compared mitochondria from a wild-type strain to mitochondria from a Δtaz1 strain that lacks tafazzin and contains lower amounts of unremodeled cardiolipin, and mitochondria from a Δcrd1 strain that lacks cardiolipin synthase and cannot synthesize cardiolipin. We found that the 13C-label from the pyruvate substrate was distributed through about twelve metabolites. Several of the identified metabolites were specific to yeast pathways, including branched chain amino acids and fusel alcohol synthesis. Most metabolites showed similar kinetics amongst the different strains but mevalonate and α-ketoglutarate, as well as the NAD+/NADH couple measured in separate nuclear magnetic resonance experiments, showed pronounced differences. Taken together, the results show that cardiolipin remodeling influences pyruvate metabolism, tricarboxylic acid cycle flux, and the levels of mitochondrial nucleotides.

Proteomic Characterization of Human Placenta: Insights into Potential Therapeutic Applications for Osteoarthritis

Biologics have become increasingly prominent as therapeutics in recent years due to their innate immune-privileged nature, biocompatibility, and high levels of protein biofactors. The aim of the study is to characterise the biologic, lyophilized human placenta (LHP) and explore its therapeutic potential for osteoarthritis (OA). The presence of six bioactive constituents that regulate cell-extracellular matrix interaction was identified by liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry (LC-ESI-QTOF/MS). Metalloproteinase inhibitor 3 (TIMP3), alpha-1 anti-trypsin (a1AT), basic fibroblast growth factor (bFGF), and transforming growth factor β1 (TGFβ1) were detected and quantified using ELISA. The total protein content present in LHP by Bradford assay was found to be 409.35 ± 0.005 μg/ml. The analytical techniques such as Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR), solid state carbon-13 Nuclear Magnetic Resonance (ssC13 NMR) spectroscopy, and Differential Scanning Calorimetry (DSC) revealed the secondary structure and conformational stability of LHP. X-Ray diffraction (XRD) studies showed its amorphous nature. Bioactivity assessment of LHP was performed in human keratinocytes (HaCaT) and human dermal fibroblasts (HDF) by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The LHP was highly proliferative against skin cells and non-toxic, based on the findings of the bioactivity assay. LHP has the potential to be used as a therapeutic agent for OA, as its characterisation unveiled its physical stability, significant concentration of bioactive components that are pertinent to cartilage repair and its conformational stability.

Integrating metabolomics into reproduction: Sperm metabolism and fertility enhancement in pigs

The last decades of research have revealed that many other factors besides gamete genomes are able to determine the reproductive outcomes. Indeed, paternal factors have been observed to be capable of modulating multiple crucial features of the reproductive process, such as sperm physiology, the maternal environment and, even, the offspring health. These recent advances have been encompassed with the emergence of OMICS technologies, as they comprehensively characterise the molecular composition of biological systems. The present narrative review aimed to take a closer look at the potential of these technologies in the field of reproductive biology. This literature revision shows that most studies up to date have followed a non-targeted approach to screen mammalian seminal plasma (SP) and sperm metabolite composition through different metabolome platforms. These studies have proposed metabolites of multiple natures as potential in vivo fertility biomarkers. Yet, targeted approaches can be used to answer specific biological question, and their power is exemplified herein. For instance, metabolomic studies have uncovered not only that glycolysis is the main ATP energy source of pig sperm, but also that sperm metabolism can trigger DNA damage, hence compromise embryo development. In conclusion, this review shows the potential of both non-targeted and targeted metabolomics for the discovery of cell pathways that govern the reproductive process. Understanding these systems could help make progress in different areas, including livestock efficient breeding, the improvement of artificial reproductive technologies, and the development of biomarkers for infertility detection.

Identification of mungbean yellow mosaic India virus and susceptibility-related metabolites in the apoplast of mung bean leaves

KEY MESSAGE

The investigation of MYMIV-infected mung bean leaf apoplast revealed viral genome presence, increased EVs secretion, and altered stress-related metabolite composition, providing comprehensive insights into plant-virus interactions. The apoplast, an extracellular space around plant cells, plays a vital role in plant-microbe interactions, influencing signaling, defense, and nutrient transport. While the involvement of apoplast and extracellular vesicles (EVs) in RNA virus infection is documented, the role of the apoplast in plant DNA viruses remains unclear. This study explores the apoplast's role in mungbean yellow mosaic India virus (MYMIV) infection. Our findings demonstrate the presence of MYMIV genomic components in apoplastic fluid, suggesting potential begomovirus cell-to-cell movement via the apoplast. Moreover, MYMIV infection induces increased EVs secretion into the apoplast. NMR-based metabolomics reveals altered metabolic profiles in both apoplast and symplast in response to MYMIV infection, highlighting key metabolites associated with stress and defense mechanisms. The data show an elevation of α- and β-glucose in both apoplast and symplast, suggesting a shift in glucose utilization. Interestingly, this increase in glucose does not contribute to the synthesis of phenolic compounds, potentially influencing the susceptibility of mung bean to MYMIV. Fructose levels increase in the symplast, while apoplastic sucrose levels rise significantly. Symplastic aspartate levels increase, while proline exhibits elevated concentration in the apoplast and reduced concentration in the cytosol, suggesting a role in triggering a hypersensitive response. These findings underscore the critical role of the apoplast in begomovirus infection, providing insights for targeted viral disease management strategies.

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.

Metabolomics-based analysis of the diatom Cheatoceros tenuissimus combining NMR and GC-MS techniques

Metabolomics, a recent addition to omics sciences, studies small molecules across plants, animals, humans, and marine organisms. Nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GC-MS) are widely used in those studies, including microalgae metabolomics. NMR is non-destructive and highly reproducible but has limited sensitivity, which could be supplemented by joining GC-MS analysis. Extracting metabolites from macromolecules requires optimization for trustworthy results. Different extraction methods yield distinct profiles, emphasizing the need for optimization. The results indicated that the optimized extraction procedure successfully identified NMR and GC-MS-based metabolites in MeOH, CHCl3, and H2O extraction solvents. The findings represented the spectral information related to carbohydrates, organic molecules, and amino acids from the water-soluble metabolites fraction and a series of fatty acid chains, lipids, and sterols from the lipid fraction. Our study underscores the benefit of combining NMR and GC-MS techniques to comprehensively understand microalgae metabolomes, including high and low metabolite concentrations and abundances.•In this study, we focused on optimizing the extraction procedure and combining NMR and GC-MS techniques to overcome the low NMR sensitivity and the different detected range limits of NMR and GC-MS.•We explored metabolome diversity in a tropical strain of the small cells' diatom Cheatoceros tenuissimus.

Identifying Anti-NSCLC Bioactive Compounds in Scutellaria via 2D NMR-Based Metabolomic Analysis of Pharmacologically Classified Crude Extracts

We presented a strategy utilizing 2D NMR-based metabolomic analysis of crude extracts, categorized by different pharmacological activities, to rapidly identify the primary bioactive components of TCM. It was applied to identify the potential bioactive components from Scutellaria crude extracts that exhibit anti-non-small cell lung cancer (anti-NSCLC) activity. Four Scutellaria species were chosen as the study subjects because of their close phylogenetic relationship, but their crude extracts exhibit significantly different anti-NSCLC activity. Cell proliferation assay was used to assess the anti-NSCLC activity of four species of Scutellaria. 1H-13C HSQC spectra were acquired for the chemical profiling of these crude extracts. Based on the pharmacological classification (PCA, OPLS-DA and univariate hypothesis test) were performed to identify the bioactive constituents in Scutellaria associated with the anti-NSCLC activity. As a result, three compounds, baicalein, wogonin and scutellarin were identified as bioactive compounds. The anti-NSCLC activity of the three potential active compounds were further confirmed via cell proliferation assay. The mechanism of the anti-NSCLC activity by these active constituents was further explored via flow cytometry and western blot analyses. This study demonstrated 2D NMR-based metabolomic analysis of pharmacologically classified crude extracts to be an efficient approach to the identification of active components of herbal medicine.

Vibrational imaging of metabolites for improved microbial cell strains

Significance

Biomanufacturing utilizes modified microbial systems to sustainably produce commercially important biomolecules for use in agricultural, energy, food, material, and pharmaceutical industries. However, technological challenges related to non-destructive and high-throughput metabolite screening need to be addressed to fully unlock the potential of synthetic biology and sustainable biomanufacturing.

Aim

This perspective outlines current analytical screening tools used in industrial cell strain development programs and introduces label-free vibrational spectro-microscopy as an alternative contrast mechanism.

Approach

We provide an overview of the analytical instrumentation currently used in the "test" portion of the design, build, test, and learn cycle of synthetic biology. We then highlight recent progress in Raman scattering and infrared absorption imaging techniques, which have enabled improved molecular specificity and sensitivity.

Results

Recent developments in high-resolution chemical imaging methods allow for greater throughput without compromising the image contrast. We provide a roadmap of future work needed to support integration with microfluidics for rapid screening at the single-cell level.

Conclusions

Quantifying the net expression of metabolites allows for the identification of cells with metabolic pathways that result in increased biomolecule production, which is essential for improving the yield and reducing the cost of industrial biomanufacturing. Technological advancements in vibrational microscopy instrumentation will greatly benefit biofoundries as a complementary approach for non-destructive cell screening.

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.

Investigating the Effectiveness of a Carb-Free Oloproteic Diet in Fibromyalgia Treatment

Fibromyalgia (FM), a chronic disease with a high incidence in women, poses a significant challenge for diagnosis and treatment, especially due to the absence of specific biomarkers and the multifaceted nature of its symptoms, which range from neuromuscular pain to mood disorders and intestinal dysbiosis. While diagnosis currently relies on rheumatological clinical evaluations and treatment options mainly focus on symptom management, FM seems to have possible links with systemic metabolic dysfunctions with a common inflammatory root. In this context, a new therapeutic avenue emerges: could a therapeutic nutritional approach be the missing piece of the puzzle? Indeed, diet therapies employed particularly for metabolic syndromes proved recently to be efficacious for correcting systemic dysmetabolism and a high number of chronic inflammation conditions. In particular, the very-low-calorie ketogenic diet (VLCKD) demonstrated therapeutic benefits in many disorders. In the present study, we aimed to investigate the specific effects of two dietary interventions, namely the oloproteic VLCKD and the low-glycemic insulinemic (LOGI) diet, on two groups of female FM patients (FM1 and FM2) over a 45-day period. Utilizing clinical and laboratory tests, as well as non-invasive NMR metabolomic analysis of serum, urine, and saliva samples, we sought to uncover how these dietary regimens impact the metabolic dysfunctions associated with FM.

Molecular Networking-Based Metabolome, In Vitro Antidiabetic and Anti-Inflammatory Effects of Breonadia salicina (Vahl) Hepper & J.R.I. Wood

Breonadia salicina (Vahl) Hepper & J.R.I. Wood is widely distributed throughout Africa. It is used ethnobotanically to treat various diseases. However, the metabolic profile of the Breonadia species is not well characterized and the metabolites that are responsible for the bioactivity of this plant remain unknown. Therefore, there is a need to determine the phytochemical and bioactivity profile to identify metabolites that contribute to the antidiabetic, anti-inflammatory and antiproliferation activity, including the genotoxicity and cytotoxic effects, of Breonadia salicina. The study is aimed at exploring the metabolomic profile antidiabetic, anti-inflammatory and antiproliferation activity, as well as the genotoxicity and cytotoxicity effects, of constituents of B. salicina. The compounds in the B. salicina extract were analyzed by ultra-performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS), and the resultant data were further analyzed using a molecular networking approach. The crude stem bark and root extracts showed the highest antidiabetic activity against α-amylase at the lowest test concentration of 62.5 µg/mL, with 74.53 ± 0.74% and 79.1 ± 1.5% inhibition, respectively. However, the crude stem bark and root extracts showed the highest antidiabetic activity against α-glucosidase at the lowest test concentration of 31.3 µg/mL, with 98.20 ± 0.15% and 97.98 ± 0.22% inhibition, respectively. The crude methanol leaf extract showed a decrease in the nitrite concentration at the highest concentration of 200 µg/mL, with cell viability of 90.34 ± 2.21%, thus showing anti-inflammatory activity. No samples showed significant cytotoxic effects at a concentration of 10 µg/mL against HeLa cells. Furthermore, a molecular network of Breonadia species using UPLC-QTOF-MS with negative mode electrospray ionization showed the presence of organic oxygen compounds, lipids, benzenoids, phenylpropanoids and polyketides. These compound classes were differentially distributed in the three different plant parts, indicating the chemical differences between the stem bark, root and leaf extracts of B. salicina. Therefore, the identified compounds may contribute to the antidiabetic and anti-inflammatory activity of Breonadia salicina. The stem bark, root and leaf extracts of B. salicina yielded thirteen compounds identified for the first time in this plant, offering a promising avenue for the discovery of new lead drugs for the treatment of diabetes and inflammation. The use of molecular networking produced a detailed phytochemical overview of this Breonadia species. The results reported in this study show the importance of searching for bioactive compounds from Breonadia salicina and provide new insights into the phytochemical characterization and bioactivity of different plant parts of Breonadia salicina.

Design, Synthesis, Antitumor Activity and NMR-Based Metabolomics of Novel Amino Substituted Tetracyclic Imidazo[4,5-b]Pyridine Derivatives

Newly prepared tetracyclic imidazo[4,5-b]pyridine derivatives were synthesized to study their antiproliferative activity against human cancer cells. Additionally, the structure-activity was studied to confirm the impact of the N atom position in pyridine nuclei as well as the chosen amino side chains on antiproliferative activity. Targeted amino substituted regioisomers were prepared by using uncatalyzed amination from corresponding chloro substituted precursors. The most active compounds 6 a, 8 and 10 showed improved activity in comparison to standard drug etoposide with IC50 values in a nanomolar range of concentration (0.2-0.9 μM). NMR-based metabolomics is a powerful instrument to elucidate activity mechanism of new chemotherapeutics. Multivariate and univariate statistical analysis of metabolic profiles of non-small cell lung cancer cells before and after exposure to 6 a revealed significant changes in metabolism of essential amino acids, glycerophospholipids and oxidative defense. Insight into the changes of metabolic pathways that are heavily involved in cell proliferation and survival provide valuable guidelines for more detailed analysis of activity metabolism and possible targets of this class of bioactive compounds.

Revealing metabolic and biochemical variations via 1H NMR metabolomics in streptozotocin-nicotinamide-induced diabetic rats treated with metformin

The increasing prevalence of lean diabetes has prompted the generation of animal models that mimic metabolic disease in humans. This study aimed to determine the optimum streptozotocin-nicotinamide (STZ-NA) dosage ratio to elicit lean diabetic features in a rat model. It also used a proton nuclear magnetic resonance (1H NMR) urinary metabolomics approach to identify the metabolic effect of metformin treatment on this novel rat model. Three different STZ-NA dosage regimens (by body weight: Group A: 110 mg/kg NA and 45 mg/kg STZ; Group B: 180 mg/kg NA and 65 mg/kg STZ and Group C: 120 mg/kg NA and 60 mg/kg STZ) were administered to Sprague-Dawley rats along with oral metformin. Group A diabetic rats (A-DC) showed favorable serum biochemical analyses and a more positive response toward oral metformin administration relative to the other STZ-NA dosage ratio groups. Orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed that glucose, citrate, pyruvate, hippurate, and methylnicotinamide differentiating the OPLS-DA of A-MTF rats (Group A diabetic rats treated with metformin) and A-DC model rats. Subsequent metabolic pathway analyses revealed that metformin treatment was associated with improvement in dysfunctions caused by STZ-NA induction, including carbohydrate metabolism, cofactor metabolism, and vitamin and amino acid metabolism. In conclusion, our results identify the best STZ-NA dosage ratio for a rat model to exhibit lean type 2 diabetic features with optimum sensitivity to metformin treatment. The data presented here could be informative to improve our understanding of non-obese diabetes in humans through the identification of possible activated metabolic pathways in the STZ-NA-induced diabetic rats model.

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.

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.

1H-NMR-based urine metabolomics of prostate cancer and benign prostatic hyperplasia

Background

Prostate cancer (PCa) and benign prostatic hyperplasia (BPH) are prevalent conditions affecting a significant portion of the male population, particularly with advancing age. Traditional diagnostic methods, such as digital rectal examination (DRE) and prostate-specific antigen (PSA) tests, have limitations in specificity and sensitivity, leading to potential overdiagnosis and unnecessary biopsies.

Significance

This study explores the effectiveness of 1H NMR urine metabolomics in distinguishing PCa from BPH and in differentiating various PCa grades, presenting a non-invasive diagnostic alternative with the potential to enhance early detection and patient-specific treatment strategies.

Results

The study demonstrated the capability of 1H NMR urine metabolomics in detecting distinct metabolic profiles between PCa and BPH, as well as among different Gleason grade groups. Notably, this method surpassed the PSA test in distinguishing PCa from BPH. Untargeted metabolomics analysis also revealed several metabolites with varying relative concentrations between PCa and BPH cases, suggesting potential biomarkers for these conditions.

Advancing Natural Product Discovery: A Structure-Oriented Fractions Screening Platform for Compound Annotation and Isolation

Natural product discovery is hindered by the lack of tools that integrate untargeted nuclear magnetic resonance and mass spectrometry data on a library scale. This article describes the first application of the innovative NMR/MS-based machine learning tool, the "Structure-Oriented Fractions Screening Platform (SFSP)", enabling functional-group-guided fractionation and accelerating the discovery and characterization of undescribed natural products. The concept was applied to the extract of a marine fungus known to be a prolific producer of diverse natural products. With the assistance of SFSP, we isolated 24 flavipidin derivatives and five phenalenone analogues from Aspergillus sp. GE2-6, revealing 27 undescribed compounds. Compounds 7-22 were proposed as isomeric derivatives featuring a 5/6-ring fusion, formed by the dimerization of flavipidin E (5). Compounds 23 and 24 were envisaged as isomeric derivatives with a 6/5/6-ring fusion, generated through the degradation of two flavipidin E molecules. Furthermore, flavipidin A (1) and asperphenalenone E (28) exhibited potent anti-influenza (PR8) activities, with IC50 values of 21.9 ± 0.2 and 12.9 ± 0.1 μM, respectively. Meanwhile, asperphenalenone (26) and asperphenalenone P (27) treatments exhibited significant inhibition of HIV pseudovirus infection in 293FT cells, boasting IC50 values of 6.1 ± 0.9 and 4.6 ± 1.1 μM, respectively. Overall, SFSP streamlines natural product isolation through NMR and MS data integration, as showcased by the discovery of numerous undescribed flavipidins and phenalenones based on NMR olefinic signals and low-field hydroxy signals.

Metabolite signature of human malignant thyroid tissue: A systematic review and meta-analysis

BACKGROUND

Thyroid cancer (TC) is the predominant malignancy within the endocrine system. However, the standard method for TC diagnosis lacks the capability to identify the pathological condition of all thyroid lesions. The metabolomics approach has the potential to manage this problem by identifying differential metabolites.

AIMS

This study conducted a systematic review and meta-analysis of the NMR-based metabolomics studies in order to identify significant altered metabolites associated with TC.

METHODS

A systematic search of published literature in any language in three databases including Embase, PubMed, and Scopus was conducted. Out of 353 primary articles, 12 studies met the criteria for inclusion in the systematic review. Among these, five reports belonging to three articles were eligible for meta-analysis. The correlation coefficient of the orthogonal partial least squares discriminant analysis, a popular model in the multivariate statistical analysis of metabolomic data, was chosen for meta-analysis. The altered metabolites were chosen based on the fact that they had been found in at least three studies.

RESULTS

In total, 49 compounds were identified, 40 of which were metabolites. The increased metabolites in thyroid lesions compared normal samples included lactate, taurine, alanine, glutamic acid, glutamine, leucine, lysine, phenylalanine, serine, tyrosine, valine, choline, glycine, and isoleucine. Lipids were the decreased compounds in thyroid lesions. Lactate and alanine were increased in malignant versus benign thyroid lesions, while, myo-inositol, scyllo-inositol, citrate, choline, and phosphocholine were found to be decreased. The meta-analysis yielded significant results for three metabolites of lactate, alanine, and citrate in malignant versus benign specimens.

DISCUSSION

In this study, we provided a concise summary of 12 included metabolomic studies, making it easier for future researchers to compare their results with the prior findings.

CONCLUSION

It appears that the field of TC metabolomics will experience notable advancement, leading to the discovery of trustworthy diagnostic and prognostic biomarkers.

Diosmin alleviates ulcerative colitis in mice by increasing Akkermansia muciniphila abundance, improving intestinal barrier function, and modulating the NF-κB and Nrf2 pathways

Ulcerative colitis is a common type of inflammatory bowel disease that affects millions of individuals around the world. Traditional UC treatment has focused on suppressing immune responses rather than treating the underlying causes of UC, which include oxidative stress, inflammation, and microbiota dysbiosis. Diosmin (DIO), a naturally occurring flavonoid, possesses antioxidant and anti-inflammatory properties. This study aimed to assess the efficacy of DIO in treating dextran-sulfate sodium (DSS)-induced colitis, and to investigate some of its underlying mechanisms, with an emphasis on Akkermansia muciniphila abundance, inflammatory markers, and intestinal barrier function. C57BL/6 mice were given 4% (w/v) DSS to induce colitis. DSS-induced mice were administered DIO (100 and 200 mg/kg) or sulfasalazine orally for 7 days. Every day, the disease activity index (DAI) was determined by recording body weight, diarrhea, and bloody stool. Changes in fecal A. muciniphila abundance, colonic MUC1 and MUC2 expression, as well as oxidative stress and inflammatory markers were all assessed. Histopathological changes, colonic PIK3PR3 and ZO-1 levels, and immunohistochemical examinations of occludin and claudin-1, were investigated. DIO administration resulted in a dose-dependent decrease in DAI, as well as increase in A. muciniphila abundance and MUC2 expression while decreasing MUC1 expression. DIO also dramatically reduced colonic oxidative stress and inflammation by regulating the NF-κB and Nrf2 cascades, restored intestinal barrier integrity by inhibiting PIK3R3 and inducing ZO-1, and improved occludin/claudin-1 gene expression and immunostaining. This study provides the first evidence that DIO preserves intestinal barrier integrity and increases A. muciniphila abundance in DSS-induced colitis. However, more research is required to explore the impact of DIO on the overall composition and diversity of the gut microbiota. Likewise, it will be important to fully understand the molecular mechanisms by which A. muciniphila maintains intestinal barrier function and its potential use as an adjuvant in the treatment of UC.

Identification and quantification of N6-methyladenosine by chemical derivatization coupled with 19F NMR spectroscopy

N 6-Methyladenosine (6mA) is a well-known prokaryotic DNA modification that has been shown to play epigenetic roles in eukaryotic DNA. Accurate detection and quantification of 6mA are prerequisites for molecular understanding of the impact of 6mA modification on DNA. However, the existing methods have several problems, such as high false-positive rate, time-consuming and complex operating procedures. Chemical sensors for the selective detection of 6mA modification are rarely reported in the literature. Fluorinated phenylboronic acid combined with 19F NMR analysis is an effective method for determining DNA or RNA modification. In this study, we presented a simple and fast chemical method for labelling the 6th imino group of 6mA using a boric-acid-derived probe. Besides, the trifluoromethyl group of trifluoromethyl phenylboronic acid (2a) could detect 6mA modification through 19F NMR. Combined with this sensor system, 6mA modification could be detected well and quickly in 6 types of deoxynucleoside mixtures and DNA samples. Taken together, the method developed in the current study has potential for specific detection of 6mA in biological samples.

AnnoSM: An Automated Annotation Tool for Determining the Substituent Modes on the Parent Skeleton Based on a Characteristic MS/MS Fragment Ion Library

Mass spectrometry (MS) is a powerful technology for the structural elucidation of known or unknown small molecules. However, the accuracy of MS-based structure annotation is still limited due to the presence of numerous isomers in complex matrices. There are still challenges in automatically interpreting the fine structure of molecules, such as the types and positions of substituents (substituent modes, SMs) in the structure. In this study, we employed flavones, flavonols, and isoflavones as examples to develop an automated annotation method for identifying the SMs on the parent molecular skeleton based on a characteristic MS/MS fragment ion library. Importantly, user-friendly software AnnoSM was built for the convenience of researchers with limited computational backgrounds. It achieved 76.87% top-1 accuracy on the 148 authentic standards. Among them, 22 sets of flavonoid isomers were successfully differentiated. Moreover, the developed method was successfully applied to complex matrices. One such example is the extract of Ginkgo biloba L. (EGB), in which 331 possible flavonoids with SM candidates were annotated. Among them, 23 flavonoids were verified by authentic standards. The correct SMs of 13 flavonoids were ranked first on the candidate list. In the future, this software can also be extrapolated to other classes of compounds.

In vivo biological validation of in silico analysis: A novel approach for predicting the effects of TLR4 exon 3 polymorphisms on brucellosis

The role of the Toll-like receptor 4 (TLR4) is of recognising intracellular and extracellular pathogens and of activating the immune response. This process can be compromised by single nucleotide polymorphisms (SNPs) which might affect the activity of several TLRs. The aim of this study is of ascertaining whether SNPs in the TLR4 of Bubalus bubalis infected by Brucella abortus, compromise the protein functionality. For this purpose, a computational analysis was performed. Next, computational predictions were confirmed by performing genotyping analysis. Finally, NMR-based metabolomics analysis was performed to identify potential biomarkers for brucellosis. The results indicate two SNPs (c. 672 A > C and c. 902 G > C) as risk factor for brucellosis in Bubalus bubalis, and three metabolites (lactate, 3-hydroxybutyrate and acetate) as biological markers for predicting the risk of developing the disease. These metabolites, together with TLR4 structural modifications in the MD2 interaction domain, are a clear signature of the immune system alteration during diverse Gram-negative bacterial infections. This suggests the possibility to extend this study to other pathogens, including Mycobacterium tuberculosis. In conclusion, this study combines multidisciplinary approaches to evaluate the biological and structural effects of SNPs on protein function.

Metabolomics and Risk of Dementia: A Systematic Review of Prospective Studies

BACKGROUND

The projected increase in the prevalence of dementia has sparked interest in understanding the pathophysiology and underlying causal factors in its development and progression. Identifying novel biomarkers in the preclinical or prodromal phase of dementia may be important for predicting early disease risk. Applying metabolomic techniques to prediagnostic samples in prospective studies provides the opportunity to identify potential disease biomarkers.

OBJECTIVE

The objective of this systematic review was to summarize the evidence on the associations between metabolite markers and risk of dementia and related dementia subtypes in human studies with a prospective design.

DESIGN

We searched PubMed, PsycINFO, and Web of Science databases from inception through December 8, 2023. Thirteen studies (mean/median follow-up years: 2.1-21.0 y) were included in the review.

RESULTS

Several metabolites detected in biological samples, including amino acids, fatty acids, acylcarnitines, lipid and lipoprotein variations, hormones, and other related metabolites, were associated with risk of developing dementia. Our systematic review summarized the adjusted associations between metabolites and dementia risk; however, our findings should be interpreted with caution because of the heterogeneity across the included studies and potential sources of bias. Further studies are warranted with well-designed prospective cohort studies that have defined study populations, longer follow-up durations, the inclusion of additional diverse biological samples, standardization of techniques in metabolomics and ascertainment methods for diagnosing dementia, and inclusion of other related dementia subtypes.

CONCLUSIONS

This study contributes to the limited systematic reviews on metabolomics and dementia by summarizing the prospective associations between metabolites in prediagnostic biological samples with dementia risk. Our review discovered additional metabolite markers associated with the onset of developing dementia and may help aid in the understanding of dementia etiology. The protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (https://www.crd.york.ac.uk/prospero/; registration ID: CRD42022357521).

Metabolomics-driven investigation of plant defense response against pest and pathogen attack

The advancement of metabolomics has assisted in the identification of various bewildering characteristics of the biological system. Metabolomics is a standard approach, facilitating crucial aspects of system biology with absolute quantification of metabolites using minimum samples, based on liquid/gas chromatography, mass spectrometry and nuclear magnetic resonance. The metabolome profiling has narrowed the wide gaps of missing information and has enhanced the understanding of a wide spectrum of plant-environment interactions by highlighting the complex pathways regulating biochemical reactions and cellular physiology under a particular set of conditions. This high throughput technique also plays a prominent role in combined analyses of plant metabolomics and other omics datasets. Plant metabolomics has opened a wide paradigm of opportunities for developing stress-tolerant plants, ensuring better food quality and quantity. However, despite advantageous methods and databases, the technique has a few limitations, such as ineffective 3D capturing of metabolites, low comprehensiveness, and lack of cell-based sampling. In the future, an expansion of plant-pathogen and plant-pest response towards the metabolite architecture is necessary to understand the intricacies of plant defence against invaders, elucidation of metabolic pathway operational during defence and developing a direct correlation between metabolites and biotic stresses. Our aim is to provide an overview of metabolomics and its utilities for the identification of biomarkers or key metabolites associated with biotic stress, devising improved diagnostic methods to efficiently assess pest and pathogen attack and generating improved crop varieties with the help of combined application of analytical and molecular tools.

Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.

Research Progress on Extraction and Detection Technologies of Flavonoid Compounds in Foods

Flavonoid compounds have a variety of biological activities and play an essential role in preventing the occurrence of metabolic diseases. However, many structurally similar flavonoids are present in foods and are usually in low concentrations, which increases the difficulty of their isolation and identification. Therefore, developing and optimizing effective extraction and detection methods for extracting flavonoids from food is essential. In this review, we review the structure, classification, and chemical properties of flavonoids. The research progress on the extraction and detection of flavonoids in foods in recent years is comprehensively summarized, as is the application of mathematical models in optimizing experimental conditions. The results provide a theoretical basis and technical support for detecting and analyzing high-purity flavonoids in foods.