NMR spectroscopy for metabolomics and metabolic profiling

Modulation of choline and lactate metabolism by basic fibroblast growth factor mitigates neuroinflammation in type 2 diabetes: Insights from 1H-NMR metabolomics analysis

BACKGROUND

Type 2 diabetes (T2D), a chronic metabolic disease, occurs brain dysfunction accompanied with neuroinflammation and metabolic disorders. The neuroprotective effects of the basic fibroblast growth factor (bFGF) have been well studied. However, the mechanism underlying the anti-inflammatory effects of bFGF remains elusive.

METHODS

In this study, db/db mice were employed as an in vivo model, while high glucose (HG)-induced SY5Y cells and LPS-induced BV2 cells were used as in vitro models. Liposomal transfection of MyD88 DNA plasmid was used for MyD88-NF-κB pathway studies. And western blotting, flow cytometry and qPCR were employed. 1H-NMR metabolomics was used to find out metabolic changes.

RESULTS

bFGF mitigated neuroinflammatory and metabolic disorders by inhibiting cortical inflammatory factor secretion and microglia hyperactivation in the cortex of db/db mice. Also, bFGF was observed to inhibit the MyD88-NF-κB pathway in high glucose (HG)-induced SY5Y cells and LPS-induced BV2 cells in in vitro experiments. Moreover, the 1H-NMR metabolomics results showed that discernible disparities between the cortical metabolic profiles of bFGF-treated db/db mice and their untreated counterparts. Notably, excessive lactate and choline deficiency attenuated the anti-inflammatory protective effect of bFGF in SY5Y cells.

CONCLUSION

bFGF ameliorates neuroinflammation in db/db mice by inhibiting the MyD88-NF-kB pathway. This finding expands the potential application of bFGF in the treatment of neuroinflammation-related cognitive dysfunction.

Single-cell sequencing to multi-omics: technologies and applications

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

NMR-based metabolomics approach to study the effect and related molecular mechanisms of Saffron essential oil against depression

Depression currently ranks as the fourth leading cause of disability globally, affecting approximately 20% of the world's population. we established a chronic restraint stress (CRS) induced depression model in mice and employed fluoxetine as a reference drug. We assessed the therapeutic potential of saffron essential oil (SEO) and elucidated its underlying mechanisms through behavioral indices and NMR-based metabolomic analysis. The findings indicate that SEO ameliorates behavioral symptoms of depression, such as the number of entries into the central area, fecal count, latency to immobility, and duration of immobility in both the Tail Suspension Test (TST) and the Forced Swim Test (FST), along with correcting the dysregulation of 5-serotonin. Metabolomic investigations identified sixteen potential biomarkers across the liver, spleen, and kidneys. SEO notably modulated nine of these biomarkers: dimethylglycine, glycerol, adenosine, β-glucose, α-glucose, uridine, mannose, sarcosine, and aspartate, with glycerol emerging as a common biomarker in both the liver and spleen. Pathway analysis suggests that these biomarkers participate in glycolysis, glycine serine threonine metabolism, and energy metabolism, potentially implicating a role in neural regulation. In summary, SEO effectively mitigates depressive-like behaviors in CRS mice, predominantly via modulation of glycolysis, amino acid metabolism, and energy metabolism, and potentially exerts antidepressant effects through neural regulation. Our study offers insights into small molecule metabolite alterations in CRS mice through a metabolomics lens, providing evidence for the antidepressant potential of plant essential oils and contributing to our understanding of the mechanisms of traditional Chinese medicine in treating depression.

Full optimization of dynamic nuclear polarization on a 1 tesla benchtop polarizer with hyperpolarizing solids

Hyperpolarization by dissolution dynamic nuclear polarization (dDNP) provides the opportunity to dramatically increase the weak nuclear magnetic resonance (NMR) signal of liquid molecular targets using the high polarization of electron radicals. Unfortunately, the solution-state hyperpolarization can only be accessed once since freezing and melting of the hyperpolarized sample happen in an irreversible fashion. A way to expand the application horizon of dDNP can therefore be to find a recyclable DNP alternative. To pursue this ambitious goal, we recently introduced the concept of recyclable hyperpolarized flow (HypFlow) DNP where hyperpolarization happens in porous hyperpolarizing solids placed in a compact benchtop DNP polarizer at a magnetic field of 1 T and a temperature of 77 K. Here we aim to optimize the radical concentrations immobilized in hyperpolarizing solids with the objective of generating as much polarization as possible in a timeframe (<1 s) compatible with future recyclable DNP applications. To do so, the solid-state DNP enhancement factors, build-up rates and DNP spectra of different hyperpolarizing solids containing various nitroxide radical loadings (20-74 μmol cm-3) are compared against the DNP performance of varying nitroxide concentrations (10-100 mM) solvated in a glassy frozen solution. We demonstrate that in <1 s, polarization enhancement goes up to 56 and 102 with surface-bound and solvated radicals, respectively, under the optimized conditions. For the range of nitroxide concentrations used cross effect DNP seems to be the dominant mechanism under benchtop conditions. This was deduced from the electron paramagnetic resonance (EPR) lineshape of TEMPOL investigated using Q-band EPR measurements.

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.

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.

Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis

Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.

High-throughput omics technologies in inflammatory bowel disease

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

Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS

Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.

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

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

Advances in mass spectrometry-based multi-scale metabolomic methodologies and their applications in biological and clinical investigations

Metabolomics is a nascent field of inquiry that emerged in the late 20th century. It encompasses the comprehensive profiling of metabolites across a spectrum of organisms, ranging from bacteria and cells to tissues. The rapid evolution of analytical methods and data analysis has greatly accelerated progress in this dynamic discipline over recent decades. Sophisticated techniques such as liquid chromatograph mass spectrometry (MS), gas chromatograph MS, capillary electrophoresis MS, and nuclear magnetic resonance serve as the cornerstone of metabolomic analysis. Building upon these methods, a plethora of modifications and combinations have emerged to propel the advancement of metabolomics. Despite this progress, scrutinizing metabolism at the single-cell or single-organelle level remains an arduous task over the decades. Some of the most thrilling advancements, such as single-cell and single-organelle metabolic profiling techniques, offer profound insights into the intricate mechanisms within cells and organelles. This allows for a comprehensive study of metabolic heterogeneity and its pivotal role in multiple biological processes. The progress made in MS imaging has enabled high-resolution in situ metabolic profiling of tissue sections and even individual cells. Spatial reconstruction techniques enable the direct representation of metabolic distribution and alteration in three-dimensional space. The application of novel metabolomic techniques has led to significant breakthroughs in biological and clinical studies, including the discovery of novel metabolic pathways, determination of cell fate in differentiation, anti-aging intervention through modulating metabolism, metabolomics-based clinicopathologic analysis, and surgical decision-making based on on-site intraoperative metabolic analysis. This review presents a comprehensive overview of both conventional and innovative metabolomic techniques, highlighting their applications in groundbreaking biological and clinical studies.

In-Cell NMR Approach for Real-Time Exploration of Pathway Versatility: Substrate Mixtures in Nonengineered Yeast

The central carbon metabolism of microbes will likely be used in future sustainable bioproduction. A sufficiently deep understanding of central metabolism would advance the control of activity and selectivity in whole-cell catalysis. Opposite to the more obvious effects of adding catalysts through genetic engineering, the modulation of cellular chemistry through effectors and substrate mixtures remains less clear. NMR spectroscopy is uniquely suited for in-cell tracking to advance mechanistic insight and to optimize pathway usage. Using a comprehensive and self-consistent library of chemical shifts, hyperpolarized NMR, and conventional NMR, we probe the versatility of cellular pathways to changes in substrate composition. Conditions for glucose influx into a minor pathway to an industrial precursor (2,3-butanediol) can thus be designed. Changes to intracellular pH can be followed concurrently, while mechanistic details for the minor pathway can be derived using an intermediate-trapping strategy. Overflow at the pyruvate level can be induced in nonengineered yeast with suitably mixed carbon sources (here glucose with auxiliary pyruvate), thus increasing glucose conversion to 2,3-butanediol by more than 600-fold. Such versatility suggests that a reassessment of canonical metabolism may be warranted using in-cell spectroscopy.

Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Cross-Platform Comparison of Amino Acid Metabolic Profiling in Three Model Organisms Used in Environmental Metabolomics

Environmental metabolomics is a promising approach to study pollutant impacts to target organisms in both terrestrial and aquatic environments. To this end, both nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based methods are used to profile amino acids in different environmental metabolomic studies. However, these two methods have not been compared directly which is an important consideration for broader comparisons in the environmental metabolomics field. We compared the quantification of 18 amino acids in the tissue extracts of Daphnia magna, a common model organism used in both ecotoxicology and ecology, using both 1H NMR spectroscopy and liquid chromatography with tandem MS (LC-MS/MS). 1H NMR quantification of amino acids agreed with the LC-MS/MS quantification for 17 of 18 amino acids measured. We also tested both quantitative methods in a D. magna sub-lethal exposure study to copper and lithium. Again, both NMR and LC-MS/MS measurements showed agreement. We extended our analyses with extracts from the earthworm Eisenia fetida and the plant model Nicotiana tabacum. The concentrations of amino acids by both 1H NMR and LC-MS/MS, agreed and demonstrated the robustness of both techniques for quantitative metabolomics. These findings demonstrate the compatibility of these two analytical platforms for amino acid profiling in environmentally relevant model organisms and emphasizes that data from either method is robust for comparisons across studies to further build the knowledge base related to pollutant exposure impacts and toxic responses of diverse environmental organisms.

Metabolomics as a Tool to Understand Nano-Plant Interactions: The Case Study of Metal-Based Nanoparticles

Metabolomics is a powerful tool in diverse research areas, enabling an understanding of the response of organisms, such as plants, to external factors, their resistance and tolerance mechanisms against stressors, the biochemical changes and signals during plant development, and the role of specialized metabolites. Despite its advantages, metabolomics is still underused in areas such as nano-plant interactions. Nanoparticles (NPs) are all around us and have a great potential to improve and revolutionize the agri-food sector and modernize agriculture. They can drive precision and sustainability in agriculture as they can act as fertilizers, improve plant performance, protect or defend, mitigate environmental stresses, and/or remediate soil contaminants. Given their high applicability, an in-depth understanding of NPs' impact on plants and their mechanistic action is crucial. Being aware that, in nano-plant interaction work, metabolomics is much less addressed than physiology, and that it is lacking a comprehensive review focusing on metabolomics, this review gathers the information available concerning the metabolomic tools used in studies focused on NP-plant interactions, highlighting the impact of metal-based NPs on plant metabolome, metabolite reconfiguration, and the reprogramming of metabolic pathways.

Absolute Quantification of Residual Solvent in Mesoporous Silica Drug Formulations Using Magic-Angle Spinning NMR Spectroscopy

Porous silica is used as a drug delivery agent to improve the bioavailability of sparsely soluble compounds. In this approach, the active pharmaceutical ingredient (API) is commonly loaded into the porous silica by incipient wetness impregnation using organic solvents. Subsequent solvent elimination is critical as the residual solvent concentration cannot exceed threshold values set by health and safety regulations (e.g., EMA/CHMP/ICH/82260/2006). For dichloromethane and methanol, for example, residual concentrations must be below 600 and 3000 ppm, respectively. Today, EU and USA Pharmacopoeias recommend tedious procedures for residual solvent quantification, requiring extraction of the solvent and subsequent quantification using capillary gas chromatography with static headspace sampling (sHS-GC). This work presents a new method based on the combination of standard addition and absolute quantification using magic-angle spinning nuclear magnetic resonance spectroscopy (MAS qNMR). The methodology was originally developed for absolute quantification of water in zeolites and has now been validated for quantification of residual solvent in drug formations using mesoporous silica loaded with ibuprofen dissolved in DCM and MeOH as test samples. Interestingly, formulations prepared using as-received or predried mesoporous silica contained 5465 versus 484.9 ppm DCM, respectively. This implies that the initial water content of the silica carrier can impact the residual solvent concentration in drug-loaded materials. This observation could provide new options to minimize the occurrence of these undesired solvents in the final formulation.

Metabolomic Study of Urine from Workers Exposed to Low Concentrations of Benzene by UHPLC-ESI-QToF-MS Reveals Potential Biomarkers Associated with Oxidative Stress and Genotoxicity

Benzene is a human carcinogen whose exposure to concentrations below 1 ppm (3.19 mg·m^-3) is associated with myelotoxic effects. The determination of biomarkers such as trans-trans muconic acid (AttM) and S-phenylmercapturic acid (SPMA) show exposure without reflecting the toxic effects of benzene. For this reason, in this study, the urinary metabolome of individuals exposed to low concentrations of benzene was investigated, with the aim of understanding the biological response to exposure to this xenobiotic and identifying metabolites correlated with the toxic effects induced by it. Ultra-efficient liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer (UHPLC-ESI-Q-ToF-MS) was used to identify metabolites in the urine of environmentally (n = 28) and occupationally exposed (n = 32) to benzene (mean of 22.1 μg·m^-3 and 31.8 μg·m^-3, respectively). Non-targeted metabolomics analysis by PLS-DA revealed nine urinary metabolites discriminating between groups and statistically correlated with oxidative damage (MDA, thiol) and genetic material (chromosomal aberrations) induced by the hydrocarbon. The analysis of metabolic pathways revealed important alterations in lipid metabolism. These results point to the involvement of alterations in lipid metabolism in the mechanisms of cytotoxic and genotoxic action of benzene. Furthermore, this study proves the potential of metabolomics to provide relevant information to understand the biological response to exposure to xenobiotics and identify early effect biomarkers.

Innovative Application of Metabolomics on Bioactive Ingredients of Foods

Metabolomics, as a new omics technology, has been widely accepted by researchers and has shown great potential in the field of nutrition and health in recent years. This review briefly introduces the process of metabolomics analysis, including sample preparation and extraction, derivatization, separation and detection, and data processing. This paper focuses on the application of metabolomics in food-derived bioactive ingredients. For example, metabolomics techniques are used to analyze metabolites in food to find bioactive substances or new metabolites in food materials. Moreover, bioactive substances have been tested in vitro and in vivo, as well as in humans, to investigate the changes of metabolites and the underlying metabolic pathways, among which metabolomics is used to find potential biomarkers and targets. Metabolomics provides a new approach for the prevention and regulation of chronic diseases and the study of the underlying mechanisms. It also provides strong support for the development of functional food or drugs. Although metabolomics has some limitations such as low sensitivity, poor repeatability, and limited detection range, it is developing rapidly in general, and also in the field of nutrition and health. At the end of this paper, we put forward our own insights on the development prospects of metabolomics in the application of bioactive ingredients in food.

The crosstalk of the human microbiome in breast and colon cancer: A metabolomics analysis

The human microbiome's role in colon and breast cancer is described in this review. Understanding how the human microbiome and metabolomics interact with breast and colon cancer is the chief area of this study. First, the role of the gut and distal microbiome in breast and colon cancer is investigated, and the direct relationship between microbial dysbiosis and breast and colon cancer is highlighted. This work also focuses on the many metabolomic techniques used to locate prospective biomarkers, make an accurate diagnosis, and research new therapeutic targets for cancer treatment. This review clarifies the influence of anti-tumor medications on the microbiota and the proactive measures that can be taken to treat cancer using a variety of therapies, including radiotherapy, chemotherapy, next-generation biotherapeutics, gene-based therapy, integrated omics technology, and machine learning.

Metabolic Fingerprinting of Fabry Disease: Diagnostic and Prognostic Aspects

Fabry disease (FD) is an X-linked lysosomal disease due to a deficiency in the activity of the lysosomal-galactosidase A (GalA), a key enzyme in the glycosphingolipid degradation pathway. FD is a complex disease with a poor genotype–phenotype correlation. In the early stages, FD could involve the peripheral nervous system (acroparesthesias and dysautonomia) and the ski (angiokeratoma), but later kidney, heart or central nervous system impairment may significantly decrease life expectancy. The advent of omics technologies offers the possibility of a global, integrated and systemic approach well-suited for the exploration of this complex disease. In this narrative review, we will focus on the main metabolomic studies, which have underscored the importance of detecting biomarkers for a diagnostic and prognostic purpose in FD. These investigations are potentially useful to explain the wide clinical, biochemical and molecular heterogeneity found in FD patients. Moreover, the quantitative mass spectrometry methods developed to evaluate concentrations of these biomarkers in urine and plasma will be described. Finally, the complex metabolic biomarker profile depicted in FD patients will be reported, which varies according to gender, types of mutations, and therapeutic treatment.

Time-Domain-Based Methyl Proton NMR with Absolute Quantitation Ability for Targeted Metabolomics

In high-throughput scenarios of targeted metabolomics, it is a significant challenge to process complex NMR spectra with severely overlapping signals produced by metabolites with similar chemical structures. Traditional frequency-domain NMR is ineffective to some degree due to the low sensitivity and poor resolution, and the precision of quantitation is usually affected by poorly or inconsistently phased and baselined spectra. Here, we established a strategy based on time-domain NMR focusing on methyl protons for targeted metabolomics. The targeted metabolomics focusing on bufadienolides for varietal discrimination of three toad venoms was conducted to demonstrate the practicability of time-domain-based methyl proton NMR. It revealed that the signals could be precisely identified and quantitated with an signal-to-noise ratio (SNR) of about 10 and a resolution of about 1.0 Hz. The sensitivity and resolution improvement make it particularly applicable in targeted metabolomics. The precise and absolute quantitation ability confirmed by triple-quadrupole mass spectrometry (QqQ-MS) could further extend its application range. Importantly, the methyl group is common in metabolites with a relatively wide chemical shift range. Time-domain analysis could be conducted in common NMR software. This technique is very easy and convenient for general researchers when employed as a complementary alternative to traditional frequency-domain NMR, especially in the field of targeted metabolomics.

Noninvasive NMR/MRS Metabolic Parameters to Evaluate Metabolic Syndrome in Rats

(1) Background: Ectopic fat deposition and its effects, metabolic syndrome, have been significantly correlated to lifestyle and caloric consumption. There is no specific noninvasive evaluation tool being used in order to establish clinical markers for tracing the metabolic pathway implicated in obesity-related abnormalities that occur in the body as a result of a high-fat diet (HFD). The purpose of this work is to investigate in vivo ectopic fat distribution and in vitro metabolite profiles given by HFDs, as well as how they are inter-related, in order to find surrogate metabolic biomarkers in the development of metabolic syndrome utilizing noninvasive approaches. (2) Methods: Male Wistar rats were divided into a standard normal chow diet, ND group, and HFD group. After 16 weeks of different diet administration, blood samples were collected for proton nuclear magnetic resonance (¹H NMR) and biochemical analysis. Magnetic resonance imaging/proton magnetic resonance spectroscopy (MRI/¹H MRS) was performed on the abdomen, liver, and psoas muscle of the rats. (3) Results: Visceral fat showed the strongest relationship with blood cholesterol. Although liver fat content (LFC) was not associated with any biophysical profiles, it had the highest correlation with metabolites such as (-CH₂)_n very-low-density lipoprotein/low-density lipoprotein (VLDL/LDL), lactate, and N-acetyl glycoprotein of serum ¹H NMR. HFD showed no obvious influence on muscle fat accumulation. Acetoacetate, N-acetyl glycoprotein, lactate, (-CH₂)_n VLDL/LDL, and valine were the five possible metabolic biomarkers used to differentiate HFD from ND in the present study. (4) Conclusions: Our study has validated the influence of long-term HFD-induced ectopic fat on body metabolism as well as the metabolic profile deterioration both in vivo and in vitro.

Cell energy metabolism and bone formation

Energy metabolism plays an important role in cell and tissue ability to effectively function, maintain homeostasis, and perform repair. Yet, the role of energy metabolism in skeletal tissues in general and in bone, in particular, remains understudied. We, here, review the aspects of cell energy metabolism relevant to bone tissue, such as: i) availability of substrates and oxygen; ii) metabolism regulatory mechanisms most active in bone tissue, e.g. HIF and BMP; iii) crosstalk of cell bioenergetics with other cell functions, e.g. proliferation and differentiation; iv) role of glycolysis and mitochondrial oxidative phosphorylation in osteogenic lineage; and v) most significant changes in bone energy metabolism observed in aging and other pathologies. In addition, we review available methods to study energy metabolism on a subcellular, cellular, tissue, and live animal levels.

Toward Nanomolar Multi-Component Analysis by 19F NMR

The widespread application of nuclear magnetic resonance (NMR) spectroscopy in detection is currently hampered by its inherently low sensitivity and complications resulting from the undesired signal overlap. Here, we report a detection scheme to address these challenges, where analytes are recognized by ¹⁹F-labeled probes to induce characteristic shifts of ¹⁹F resonances that can be used as "chromatographic" signatures to pin down each low-concentration analyte in complex mixtures. This unique signal transduction mechanism allows detection sensitivity to be enhanced by using massive chemically equivalent ¹⁹F atoms, which was achieved through the proper installation of nonafluoro-tert-butoxy groups on probes of high structural symmetry. It is revealed that the binding of an analyte to the probe can be sensed by as many as 72 chemically equivalent ¹⁹F atoms, allowing the quantification of analytes at nanomolar concentrations to be routinely performed by NMR. Applications on the detection of trace amounts of prohibited drug molecules and water contaminants were demonstrated. The high sensitivity and robust resolving ability of this approach represent a first step toward extending the application of NMR to scenarios that are now governed by chromatographic and mass spectrometry techniques. The detection scheme also makes possible the highly sensitive non-invasive multi-component analysis that is difficult to achieve by other analytical methods.

Studying Metabolism by NMR-Based Metabolomics

During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

Profiling metabolites and lipoproteins in COMETA, an Italian cohort of COVID-19 patients

Metabolomics and lipidomics have been used in several studies to define the biochemical alterations induced by COVID-19 in comparison with healthy controls. Those studies highlighted the presence of a strong signature, attributable to both metabolites and lipoproteins/lipids. Here, 1H NMR spectra were acquired on EDTA-plasma from three groups of subjects: i) hospitalized COVID-19 positive patients (≤21 days from the first positive nasopharyngeal swab); ii) hospitalized COVID-19 positive patients (>21 days from the first positive nasopharyngeal swab); iii) subjects after 2–6 months from SARS-CoV-2 eradication. A Random Forest model built using the EDTA-plasma spectra of COVID-19 patients ≤21 days and Post COVID-19 subjects, provided a high discrimination accuracy (93.6%), indicating both the presence of a strong fingerprint of the acute infection and the substantial metabolic healing of Post COVID-19 subjects. The differences originate from significant alterations in the concentrations of 16 metabolites and 74 lipoprotein components. The model was then used to predict the spectra of COVID-19>21 days subjects. In this group, the metabolite levels are closer to those of the Post COVID-19 subjects than to those of the COVID-19≤21 days; the opposite occurs for the lipoproteins. Within the acute phase patients, characteristic trends in metabolite levels are observed as a function of the disease severity. The metabolites found altered in COVID-19≤21 days patients with respect to Post COVID-19 individuals overlap with acute infection biomarkers identified previously in comparison with healthy subjects. Along the trajectory towards healing, the metabolome reverts back to the “healthy” state faster than the lipoproteome.

Cerebrospinal Fluid Metabolome in Parkinson's Disease and Multiple System Atrophy

Parkinson’s disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers.

Reliable budding pattern classification of yeast cells with time-resolved measurement of metabolite production

Filamentous growth in Saccharomyces cerevisiae is a stress response commonly induced under nutrient deprivation and by certain alcohols. It is a compound phenotype characterized by pseudohyphal growth, invasion and a shift to more polarized budding. Previous methods have not allowed the time-resolved determination of filamentous growth. Here we present a new method for budding pattern characterization that enables the measurement of filamentous growth and metabolite concentration during yeast cell growth at precise time intervals. By combining chemical cell immobilization and single-cell imaging using an oCelloScope™, this method provides more accurate budding pattern classification compared with previous methods. The applications of the method include, for example, investigation of quorum sensing-controlled yeast filamentous growth and metabolism under stress and identification of toxic metabolites.

NMR technique and methodology in botanical health product analysis and quality control

Botanicals have played an important role in maintaining human health and well-being throughout history. During the past few decades in particular, the use of botanical health products has gained more popularity. Whereas, quality, safety and efficacy concerns have continuously been critical issues due to the intrinsic chemical complexity of botanicals. Chemical analytical technologies play an imperative role in addressing these issues. Nuclear magnetic resonance (NMR) spectroscopy has proven to be a powerful and useful tool for the investigation of botanical health products. In this review, NMR techniques and methodologies that have been successfully applied to the research and development of botanical health products in all stages, from plants to products, are discussed and summarized. Furthermore, applications of NMR together with other analytical techniques in a variety of domains of botanical health products investigation, such as plant species differentiation, adulteration detection, and bio-activity evaluation, are discussed and illustrated with typical examples. This article provides an overview of the potential uses of NMR techniques and methodologies in an attempt to further promote their recognition and utilization in the field of botanical health products analysis and quality control.

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

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

Extending the Scope of 1H NMR-Based Blood Metabolomics for the Analysis of Labile Antioxidants: Reduced and Oxidized Glutathione

Glutathione is a ubiquitous cellular antioxidant, which is critically required to protect cells from oxidative damage and free radical injury. It is practically impossible to analyze glutathione in its native form after isolation from biological mixtures since the active form (reduced glutathione, GSH) spontaneously gets converted to the oxidized form (oxidized glutathione, GSSG). To address this challenge, numerous highly sensitive detection methods, including mass spectrometry, have been used in conjunction with derivatization to block the oxidation of GSH. Efforts so far to quantitate GSH and GSSG using the nuclear magnetic resonance (NMR) spectroscopy method have remained unsuccessful. With a focus on addressing this challenge, in this study, we describe an extension to our recent whole blood analysis method [ Anal. Chem. 2017, 89, 4620-4627] that includes the important antioxidants GSH and GSSG. Fresh and frozen human whole blood specimens as well as standard GSH and GSSG were comprehensively investigated using NMR without and with derivatization using N-ethylmaleimide (NEM). NMR experiments detect two diastereomers, distinctly, for the derivatized GSH and enable the analysis of both GSH and GSSG in human whole blood with an accuracy of >99%. Interestingly, the excess (unreacted) NEM used for blocking the GSH can be removed from the samples during a drying step after extraction, with no need for additional processing. This is an important characteristic that offers an added advantage for simultaneous analysis of the antioxidants (GSH and GSSG), redox coenzymes (oxidized nicotinamide adenine dinucleotide (NAD⁺), reduced nicotinamide adenine dinucleotide (NADH), oxidized nicotinamide adenine dinucleotide phosphate (NADP⁺), reduced nicotinamide adenine dinucleotide phosphate (NADPH)), energy coenzymes (adenosine 5'-triphosphate (ATP), adenosine 5'-diphosphate (ADP), adenosine 5'-monophosphate (AMP)), and a large number of other blood metabolites using the same one-dimensional (1D) NMR spectrum. The presented method broadens the scope of global metabolite profiling and adds a new dimension to NMR-based blood metabolomics. Further, the method demonstrated here for human blood can be extended to virtually any biological specimen.

A General Reconstruction Method for Multidimensional Sparse Sampling Nuclear Magnetic Resonance Spectroscopy

Multidimensional NMR spectroscopy provides a powerful tool for structure elucidation and dynamic analysis of complex samples, particularly for biological macromolecules. Multidimensional sparse sampling effectively accelerates NMR experiments while an efficient reconstruction method is generally required for unraveling spectra. Various reconstruction methods were proposed for pure Fourier NMR (only involving chemical shifts and J couplings detection). However, reconstruction concerned with Laplace-related NMR (i.e., involving relaxation or diffusion detection) is more challenging due to its ill-posed property. The existing Laplace-related NMR sparse sampling reconstruction methods suffer from poor resolution and possible artifacts in the resulting spectra owing to the pitfalls of the optimization algorithms. Herein, we propose a general approach for fast high-resolution reconstruction of multidimensional sparse sampling NMR, including pure Fourier, mixed Fourier-Laplace, and pure Laplace NMR, benefiting from the comprehensive sparse constraint and effective optimization algorithm and thus showing the promising prospects of multidimensional NMR.

1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophy

Background

5q spinal muscular atrophy (SMA) is a disabling and life-limiting neuromuscular disease. In recent years, novel therapies have shown to improve clinical outcomes. Yet, the absence of reliable biomarkers renders clinical assessment and prognosis of possibly already affected newborns with a positive newborn screening result for SMA imprecise and difficult. Therapeutic decisions and stratification of individualized therapies remain challenging, especially in symptomatic children. The aim of this proof-of-concept and feasibility study was to explore the value of ¹H-nuclear magnetic resonance (NMR)-based metabolic profiling in identifying non-invasive diagnostic and prognostic urinary fingerprints in children and adolescents with SMA.

Results

Urine samples were collected from 29 treatment-naïve SMA patients (5 pre-symptomatic, 9 SMA 1, 8 SMA 2, 7 SMA 3), 18 patients with Duchenne muscular dystrophy (DMD) and 444 healthy controls. Using machine-learning algorithms, we propose a set of prediction models built on urinary fingerprints that showed potential diagnostic value in discriminating SMA patients from controls and DMD, as well as predictive properties in separating between SMA types, allowing predictions about phenotypic severity. Interestingly, preliminary results of the prediction models suggest additional value in determining biochemical onset of disease in pre-symptomatic infants with SMA identified by genetic newborn screening and furthermore as potential therapeutic monitoring tool.

Conclusions

This study provides preliminary evidence for the use of ¹H-NMR-based urinary metabolic profiling as diagnostic and prognostic biomarker in spinal muscular atrophy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-021-02075-x.

Metabolomics to understand placental biology: Where are we now?

Metabolomics, the application of analytical chemistry methodologies to survey the chemical composition of a biological system, is used to globally profile and compare metabolites in one or more groups of samples. Given that metabolites are the terminal end-products of cellular metabolic processes, or 'phenotype' of a cell, tissue, or organism, metabolomics is valuable to the study of the maternal-fetal interface as it has the potential to reveal nuanced complexities of a biological system as well as differences over time or between individuals. The placenta acts as the primary site of maternal-fetal exchange, the success of which is paramount to growth and development of offspring during pregnancy and beyond. Although the study of metabolomics has proven moderately useful for the screening, diagnosis, and understanding of the pathophysiology of pregnancy complications, the placental metabolome in the context of a healthy pregnancy remains poorly characterized and understood. Herein, we discuss the technical aspects of metabolomics and review the current literature describing the placental metabolome in human and animal models, in the context of health and disease. Finally, we highlight areas for future opportunities in the emerging field of placental metabolomics.

Gut microbiome and metabolic response in non-alcoholic fatty liver disease

Fatty liver disease (FLD) is one of the largest burdens to human health worldwide and is associated with gut microbiome and metabolite stability. Engineered liver tissues have shown promise in restoring liver functions in non-alcoholic FLD (NAFLD), hepatitis and cirrhosis. Fatty liver, largely noted in obesity and hepatic cancer, is highly fatal and has led to a global increase in death rates. It is associated with complex metabolic reprogramming too. A standard approach to therapy in the newly diagnosed setting includes surgery or identification of biomarkers/ metabolites for therapeutic purposes, which ultimately focus on improvement of liver health in patients. As such there are no standard procedures for patient care, but depending on the severity, systemic therapy with either genomic, proteomic or metabolomic profiling form potential options. Better comparisons and study of underlying mechanisms in gut microbiome-based metabolic functions in obesity are urgently required. Today, an emerging field, focusing on metabolomic approaches and metabolic phenotyping, involved in high-throughput identification of metabolome in obesity and gut disorders, is involved in biomarker and metabolite identification. There are supporting technologies and approaches in NAFLD that throw light on the metabolites and gut microbiome, and also on the understanding of the risk factors of obesity along with liver cancer metabolic reaction networks. We discuss the current state of NAFLD metabolites, gut micro-environmental changes, and the further challenges in digital metabolomics profiling. Innovative clinical trial designs, with biomarker-enrichment strategies that are required to improve the outcome of NAFLD in patients are also discussed.

Metabolomic Studies for Metabolic Alterations Induced by Non-Steroidal Anti-Inflammatory Drugs: Mini Review

Non-steroidal anti-inflammatory drugs (NSAIDs) are Food and Drug Administration (FDA) approved antipyretic, anti-inflammatory, and analgesic drugs to mitigate pain, however it is associated with gastrointestinal injury and cardiovascular disease in some individuals. Metabolomics has the potential to understand the interaction of host and the drugs, such as NSAIDs administration. This discipline has been used by many researchers to understand the serious side effects of NSAIDs. We highlighted (1) the potential of metabolomics in understanding the pathogenesis of adverse events due to NSAIDs administration; (2) choice of metabolomics techniques, bio sample handling; (3) review of metabolomics studies in the front of NSAIDs in different biofluids and tissues; (4) pathway analysis of the data presented in the published literature. In our analysis we find tricarboxylic acid cycle (TCA), "glycine serine and threonine metabolism," "alanine, aspartate, and glutamate metabolism," and fatty acid metabolism to be altered by the NSAIDs like ibuprofen, indomethacin, naproxen, aspirin, and celecoxib. In conclusion, metabolomics allows the use of biological samples to identify useful pathways involved in disease progression, and subsequently inform a greater understanding of the disease pathogenesis. A further in-depth investigation of the associated pathways mentioned above holds the potential for drug targets for side effects mitigation.

Advanced Molecular Imaging (MRI/MRS/1H NMR) for Metabolic Information in Young Adults with Health Risk Obesity

BACKGROUND

Obesity or being overweight is a medical condition of abnormal body fat accumulation which is associated with a higher risk of developing metabolic syndrome. The distinct body fat depots on specific parts of the anatomy have unique metabolic properties and different types of regional excessive fat distribution can be a disease hazard. The aim of this study was to identify the metabolome and molecular imaging phenotypes among a young adult population.

METHODS

The amount and distribution of fat and lipid metabolites profile in the abdomen, liver, and calf muscles of 46 normal weight, 17 overweight, and 13 obese participants were acquired using MRI and MR spectroscopy (MRS), respectively. The serum metabolic profile was obtained using proton NMR spectroscopy. NMR spectra were integrated into seven integration regions, which reflect relative metabolites.

RESULTS

A significant metabolic disorder symptom appeared in the overweight and obese group, and increased lipid deposition occurred in the abdomen, hepatocytes, and muscles that were statistically significant. Overall, the visceral fat depots had a marked influence on dyslipidemia biomarkers, blood triglyceride (r = 0.592, p < 0.001), and high-density lipoprotein cholesterol (r = -0.484, p < 0.001). Intrahepatocellular lipid was associated with diabetes predictors for hemoglobin (HbA1c%; r = 0.379, p < 0.001) and for fasting blood sugar (r = 0.333, p < 0.05). The lipid signals in serum triglyceride and glucose signals gave similar correspondence to biochemical lipid profiles.

CONCLUSIONS

This study proves the association between alteration in metabolome in young adults, which is the key population for early prevention of obesity and metabolic syndrome. This study suggests that dyslipidemia prevalence is influenced mainly by the visceral fat depot, and liver fat depot is a key determinant for glucose metabolism and hyperglycemia. Moreover, noninvasive advanced molecular imaging completely elucidated the impact of fat distribution on the anthropometric and laboratory parameters, especially indices of the metabolic syndrome biomarkers in young adults.

Metabolomics Approaches for the Comprehensive Evaluation of Fermented Foods: A Review

Fermentation is an important process that can provide new flavors and nutritional and functional foods, to deal with changing consumer preferences. Fermented foods have complex chemical components that can modulate unique qualitative properties. Consequently, monitoring the small molecular metabolites in fermented food is critical to clarify its qualitative properties and help deliver personalized nutrition. In recent years, the application of metabolomics to nutrition research of fermented foods has expanded. In this review, we examine the application of metabolomics technologies in food, with a primary focus on the different analytical approaches suitable for food metabolomics and discuss the advantages and disadvantages of these approaches. In addition, we summarize emerging studies applying metabolomics in the comprehensive analysis of the flavor, nutrition, function, and safety of fermented foods, as well as emphasize the applicability of metabolomics in characterizing the qualitative properties of fermented foods.

Quantitative Monitoring of the Fermentation Process of a Barley Malt Mash by Benchtop 1H NMR Spectroscopy

In order to investigate benchtop NMR spectroscopy as a monitoring tool for fermentation processes, we used a barley malt mash and took various samples over time and analysed them by NMR spectroscopy with 3-(trimethylsilyl)-2,2,3,3-tetradeuteropropionic acid sodium salt (TSP-d4) as an internal standard for the quantification of ethanol and validated the results by two different enzymatic standard test kits for ethanol analysis. We could show that the results between NMR spectroscopy and test kits were consistent with NMR having a much lower standard deviation. Finally, we discussed the applicability of the method as well as the possibility to quantify various other substances.

Identification of Metabolic Phenotypes in Young Adults with Obesity by 1H NMR Metabolomics of Blood Serum

(1) Since the obesity prevalence rate has been consistently increasing, it is necessary to find an effective way to prevent and treat it. Although progress is being made to reduce obesity in the young adult population, a better understanding of obesity-related metabolomics and related biochemical mechanisms is urgently needed for developing appropriate screening strategies. Therefore, the aim of this study is to identify the serum metabolic profile associated with young adult obesity and its metabolic phenotypes. (2) Methods: The serum metabolic profile of 30 obese and 30 normal-weight young adults was obtained using proton nuclear magnetic resonance spectroscopy (¹H NMR). ¹H NMR spectra were integrated into 24 integration regions, which reflect relative metabolites, and were used as statistical variables. (3) Results: The obese group showed increased levels of lipids, glucose, glutamate, N-acetyl glycoprotein, alanine, lactate, 3 hydroxybutyrate and branch chain amino acid (BCAA), and decreased levels of choline as compared with the normal-weight group. Non-hyperlipidemia obese adults showed lower levels of lipids and lactate, glutamate, acetoacetate, N-acetyl glycoprotein, isoleucine, and higher levels of choline and glutamine, as compared with hyperlipidemic obese adults. (4) Conclusions: This study reveals valuable findings in the field of metabolomics and young adult obesity. We propose several serum biomarkers that distinguish between normal weight and obese adults, i.e., glutamine (higher in the normal group, p < 0.05), and lactate, BCAAs, acetoacetate and 3-hydroxybutyrate (higher in the obese group, p < 0.05). In addition, visceral fat and serum TG, glutamate, acetoacetate, N-acetyl glycoprotein, unsaturated lipid, isoleucine, and VLDL/LDL are higher (p < 0.05) in the obese with hyperlipidemia. Therefore, they can be used as biomarkers to identify these two types of obesity.

High-Resolution Reconstruction for Multidimensional Laplace NMR

As a perfect complement to conventional NMR that aims for chemical structure elucidation, Laplace NMR constitutes a powerful technique to study spin relaxation and diffusion, revealing information on molecular motions and spin interactions. Different from conventional NMR adopting Fourier transform to deal with the acquired data, Laplace NMR relies on specially designed signal processing and reconstruction algorithms resembling the inverse Laplace transform, and it generally faces severe challenges in cases where high spectral resolution and high spectral dimensionality are required. Herein, based on the tensor technique for high-dimensional problems and the sparsity assumption, we propose a general method for high-resolution reconstruction of multidimensional Laplace NMR data. We show that the proposed method can reconstruct multidimensional Laplace NMR spectra in a high-resolution manner for exponentially decaying relaxation and diffusion data acquired by commercial NMR instruments. Therefore, it would broaden the scope of multidimensional Laplace NMR applications.

Stool metabolome-microbiota evaluation among children and adolescents with obesity, overweight, and normal-weight using 1H NMR and 16S rRNA gene profiling

Characterization of metabolites and microbiota composition from human stool provides powerful insight into the molecular phenotypic difference between subjects with normal weight and those with overweight/obesity. The aim of this study was to identify potential metabolic and bacterial signatures from stool that distinguish the overweight/obesity state in children/adolescents. Using ¹H NMR spectral analysis and 16S rRNA gene profiling, the fecal metabolic profile and bacterial composition from 52 children aged 7 to 16 was evaluated. The children were classified into three groups (16 with normal-weight, 17 with overweight, 19 with obesity). The metabolomic analysis identified four metabolites that were significantly different (p < 0.05) among the study groups based on one-way ANOVA testing: arabinose, butyrate, galactose, and trimethylamine. Significantly different (p < 0.01) genus-level taxa based on edgeR differential abundance tests were genus Escherichia and Tyzzerella subgroup 3. No significant difference in alpha-diversity was detected among the three study groups, and no significant correlations were found between the significant taxa and metabolites. The findings support the hypothesis of increased energy harvest in obesity by human gut bacteria through the growing observation of increased fecal butyrate in children with overweight/obesity, as well as an increase of certain monosaccharides in the stool. Also supported is the increase of trimethylamine as an indicator of an unhealthy state.

Rapid nuclear magnetic resonance data acquisition with improved resolution and sensitivity for high-throughput metabolomic analysis

Nuclear magnetic resonance (NMR)-based metabolomics has witnessed rapid advancements in recent years with the continuous development of new methods to enhance the sensitivity, resolution, and speed of data acquisition. Some of the approaches were earlier used for peptide and protein resonance assignments and have now been adapted to metabolomics. At the same time, new NMR methods involving novel data acquisition techniques, suited particularly for high-throughput analysis in metabolomics, have been developed. In this review, we focus on the different sampling strategies or data acquisition methods that have been developed in our laboratory and other groups to acquire NMR spectra rapidly with high sensitivity and resolution for metabolomics. In particular, we focus on the use of multiple receivers, phase modulation NMR spectroscopy, and fast-pulsing methods for identification and assignments of metabolites.

Molecular Identification of Endophytic Bacteria in Leucojum aestivum In Vitro Culture, NMR-Based Metabolomics Study and LC-MS Analysis Leading to Potential Amaryllidaceae Alkaloid Production

In this study, endophytic bacteria belonging to the Bacillus genus were isolated from in vitro bulblets of Leucojum aestivum and their ability to produce Amaryllidaceae alkaloids was studied. Proton Nuclear Magnetic Resonance (¹H NMR)-based metabolomics combined with multivariate data analysis was chosen to compare the metabolism of this plant (in vivo bulbs, in vitro bulblets) with those of the endophytic bacteria community. Primary metabolites were quantified by quantitative ¹H NMR (qNMR) method. The results showed that tyrosine, one precursor of the Amaryllidaceae alkaloid biosynthesis pathway, was higher in endophytic extract compared to plant extract. In total, 22 compounds were identified including five molecules common to plant and endophyte extracts (tyrosine, isoleucine, valine, fatty acids and tyramine). In addition, endophytic extracts were analyzed using Liquid Chromatography-Mass Spectrometry (LC-MS) and Gas Chromatography-Mass Spectrometry (GC-MS) for the identification of compounds in very low concentrations. Five Amaryllidaceae alkaloids were detected in the extracts of endophytic bacteria. Lycorine, previously detected by ¹H NMR, was confirmed with LC-MS analysis. Tazettine, pseudolycorine, acetylpseudolycorine, 1,2-dihydro-chlidanthine were also identified by LC-MS using the positive ionization mode or by GC-MS. In addition, 11 primary metabolites were identified in the endophytic extracts such as tyramine, which was obtained by decarboxylation of tyrosine. Thus, Bacillus sp. isolated from L. aestivum bulblets synthesized some primary and specialized metabolites in common with the L.aestivum plant. These endophytic bacteria are an interesting new approach for producing the Amaryllidaceae alkaloid such as lycorine.

Recent Advances of Microbiome-Associated Metabolomics Profiling in Liver Disease: Principles, Mechanisms, and Applications

Advances in high-throughput screening of metabolic stability in liver and gut microbiota are able to identify and quantify small-molecule metabolites (metabolome) in different cellular microenvironments that are closest to their phenotypes. Metagenomics and metabolomics are largely recognized to be the “-omics” disciplines for clinical therapeutic screening. Here, metabolomics activity screening in liver disease (LD) and gut microbiomes has significantly delivered the integration of metabolomics data (i.e., a set of endogenous metabolites) with metabolic pathways in cellular environments that can be tested for biological functions (i.e., phenotypes). A growing literature in LD and gut microbiomes reports the use of metabolites as therapeutic targets or biomarkers. Although growing evidence connects liver fibrosis, cirrhosis, and hepatocellular carcinoma, the genetic and metabolic factors are still mainly unknown. Herein, we reviewed proof-of-concept mechanisms for metabolomics-based LD and gut microbiotas’ role from several studies (nuclear magnetic resonance, gas/lipid chromatography, spectroscopy coupled with mass spectrometry, and capillary electrophoresis). A deeper understanding of these axes is a prerequisite for optimizing therapeutic strategies to improve liver health.

Metabolic, Phenotypic, and Neuropathological Characterization of the Tg4-42 Mouse Model for Alzheimer's Disease

BACKGROUND

Preclinical Alzheimer's disease (AD) research strongly depends on transgenic mouse models that display major symptoms of the disease. Although several AD mouse models have been developed representing relevant pathologies, only a fraction of available mouse models, like the Tg4-42 mouse model, display hippocampal atrophy caused by the death of neurons as the key feature of AD. The Tg4-42 mouse model is therefore very valuable for use in preclinical research. Furthermore, metabolic biomarkers which have the potential to detect biochemical changes, are crucial to gain deeper insights into the pathways, the underlying pathological mechanisms and disease progression.

OBJECTIVE

We thus performed an in-depth characterization of Tg4-42 mice by using an integrated approach to analyze alterations of complex biological networks in this AD in vivo model.

METHODS

Therefore, untargeted NMR-based metabolomic phenotyping was combined with behavioral tests and immunohistological and biochemical analyses.

RESULTS

Our in vivo experiments demonstrate a loss of body weight increase in homozygous Tg4-42 mice over time as well as severe impaired learning behavior and memory deficits in the Morris water maze behavioral test. Furthermore, we found significantly altered metabolites in two different brain regions and metabolic changes of the glutamate/4-aminobutyrate-glutamine axis. Based on these results, downstream effects were analyzed showing increased Aβ42 levels, increased neuroinflammation as indicated by increased astro- and microgliosis as well as neuronal degeneration and neuronal loss in homozygous Tg4-42 mice.

CONCLUSION

Our study provides a comprehensive characterization of the Tg4-42 mouse model which could lead to a deeper understanding of pathological features of AD. Additionally this study reveals changes in metabolic biomarker which set the base for future preclinical studies or drug development.