Evaluation of Full-Resolution J-Resolved 1H NMR Projections of Biofluids for Metabonomics Information Retrieval and Biomarker Identification

Understanding metabolic characteristics and molecular mechanisms of large to giant congenital melanocytic nevi: implications for melanoma risk and therapeutic targets

Large to giant congenital melanocytic nevi (LGCMN) present clinical challenges due to their complex phenotypic heterogeneity and increased melanoma risk. Molecular-level research is essential for understanding the pathogenic mechanisms of LGCMN and identifying potential therapeutic targets. Tissue samples from 67 LGCMN lesions and 49 matched controls were analyzed using metabolomics and transcriptomics to identify metabolic characteristics and gene expression differences. A protein-protein interaction network and a multi-layer network of key metabolites-genes-pathways were established to explore the metabolic characteristics and gene associations with LGCMN. Metabolic analysis revealed a consistent dysregulation in amino acid metabolisms, including arginine, alanine, aspartate, glutamate, phenylalanine, and tyrosine, across LGCMN lesions and subtypes. Compared to controls, 18 upregulated metabolites and 7 downregulated metabolites were identified in LGCMN lesions. Metabolic profiles varied among LGCMN subtypes, with the trunks subtype exhibiting significant alterations in branched-chain amino acids. Network analysis identified 23 genes related to melanogenesis and amino acid metabolism, including TYR, SOX10, and MITF, which showed strong correlation with tyrosine, phenylalanine, and branched-chain amino acids (r > 0.6). High centrality values for genes (e.g., EDNRB, TYR, MITF, SOX10, and MAPK3 > 0.300) and amino acids (e.g., tyrosine at 0.397 and phenylalanine at 0.374) emphasize their pivotal roles in melanogenesis. This study reveals significant metabolic and molecular differences between LGCMN lesions, normal skin, and across LGCMN subtypes, highlighting the deregulation of amino acid metabolism and key genes involved in melanogenesis. These insights enhance our understanding of LGCMN's biological heterogeneity and provide novel avenues for therapeutic intervention.

Metabolic Pattern of Brain Death-NMR-Based Metabolomics of Cerebrospinal Fluid

The aim of this study was to gain insight into the biochemical status of cerebrospinal fluid in the presence of brain death in life-supported patients. The biochemical status was determined via in vitro NMR spectroscopy of cerebrospinal fluid (CSF) obtained by lumbar puncture from 22 patients with confirmed brain death and compared with that of 34 control patients (without neurological diseases). Forty-one NMR signals from raw CSF samples and 20 signals from lipid extracts were analyzed using univariate and multivariate statistical methods. ANOVA revealed significant differences in all analyzed signals. No single biochemical marker was found to predict brain death. The CSF metabolic profiles of patients who died differed significantly from those of patients in the control group. There were many statistically significantly different compounds, including amino acids, ketone bodies, lactate, pyruvate, citrate, guanidinoacetate, choline, and glycerophosphocholine. Analysis of lipids revealed significant differences in cholesterol, estriol, and phosphoethanolamine. Discriminant analysis allows the analysis of metabolic profiles instead of single biomarkers of cerebrospinal fluid compounds. The results of our analysis allowed us to split the groups-the control group, which consisted of patients with a normal biochemical CSF composition, and the brain death group-with confirmed brain death.

MetAssimulo 2.0: a web app for simulating realistic 1D and 2D metabolomic 1H NMR spectra

MOTIVATION

Metabolomics extensively utilizes nuclear magnetic resonance (NMR) spectroscopy due to its excellent reproducibility and high throughput. Both 1D and 2D NMR spectra provide crucial information for metabolite annotation and quantification, yet present complex overlapping patterns which may require sophisticated machine learning algorithms to decipher. Unfortunately, the limited availability of labeled spectra can hamper application of machine learning, especially deep learning algorithms which require large amounts of labeled data. In this context, simulation of spectral data becomes a tractable solution for algorithm development.

RESULTS

Here, we introduce MetAssimulo 2.0, a comprehensive upgrade of the MetAssimulo 1.b metabolomic 1H NMR simulation tool, reimplemented as a Python-based web application. Where MetAssimulo 1.0 only simulated 1D 1H spectra of human urine, MetAssimulo 2.0 expands functionality to urine, blood, and cerebral spinal fluid, enhancing the realism of blood spectra by incorporating a broad protein background. This enhancement enables a closer approximation to real blood spectra, achieving a Pearson correlation of approximately 0.82. Moreover, this tool now includes simulation capabilities for 2D J-resolved (J-Res) and Correlation Spectroscopy spectra, significantly broadening its utility in complex mixture analysis. MetAssimulo 2.0 simulates both single, and groups, of spectra with both discrete (case-control, e.g. heart transplant versus healthy) and continuous (e.g. body mass index) outcomes and includes inter-metabolite correlations. It thus supports a range of experimental designs and demonstrating associations between metabolite profiles and biomedical responses.By enhancing NMR spectral simulations, MetAssimulo 2.0 is well positioned to support and enhance research at the intersection of deep learning and metabolomics.

AVAILABILITY AND IMPLEMENTATION

The code and the detailed instruction/tutorial for MetAssimulo 2.0 is available at https://github.com/yanyan5420/MetAssimulo_2.git. The relevant NMR spectra for metabolites are deposited in MetaboLights with accession number MTBLS12081.

Feed Components and Timing to Improve the Feed Conversion Ratio for Sustainable Aquaculture Using Starch

Aquaculture contributes to the sustainable development of food security, marine resource conservation, and economy. Shifting aquaculture feed from fish meal and oil to terrestrial plant derivatives may result in cost savings. However, many carnivorous fish cannot be sustained on plant-derived materials, necessitating the need for the identification of important factors for farmed fish growth and the identification of whether components derived from terrestrial plants can be used in feed. Herein, we focused on the carnivorous fish leopard coral grouper (P. leopardus) to identify the essential growth factors and clarify their intake timing from feeds. Furthermore, we evaluated the functionality of starch, which are easily produced by terrestrial plants. Results reveal that carbohydrates, which are not considered essential for carnivorous fish, can be introduced as a major part of an artificial diet. The development of artificial feed using starch offers the possibility of increasing the growth of carnivorous fish in aquaculture.

Implementation of pure shift 1 H NMR in metabolic phenotyping for structural information recovery of biofluid metabolites with complex spin systems

NMR spectroscopy is a mainstay of metabolic profiling approaches to investigation of physiological and pathological processes. The one-dimensional proton pulse sequences typically used in phenotyping large numbers of samples generate spectra that are rich in information but where metabolite identification is often compromised by peak overlap. Recently developed pure shift (PS) NMR spectroscopy, where all J-coupling multiplicities are removed from the spectra, has the potential to simplify the complex proton NMR spectra that arise from biosamples and hence to aid metabolite identification. Here we have evaluated two complementary approaches to spectral simplification: the HOBS (band-selective with real-time acquisition) and the PSYCHE (broadband with pseudo-2D interferogram acquisition) pulse sequences. We compare their relative sensitivities and robustness for deconvolving both urine and serum matrices. Both methods improve resolution of resonances ranging from doublets, triplets and quartets to more complex signals such as doublets of doublets and multiplets in highly overcrowded spectral regions. HOBS is the more sensitive method and takes less time to acquire in comparison with PSYCHE, but can introduce unavoidable artefacts from metabolites with strong couplings, whereas PSYCHE is more adaptable to these types of spin system, although at the expense of sensitivity. Both methods are robust and easy to implement. We also demonstrate that strong coupling artefacts contain latent connectivity information that can be used to enhance metabolite identification. Metabolite identification is a bottleneck in metabolic profiling studies. In the case of NMR, PS experiments can be included in metabolite identification workflows, providing additional capability for biomarker discovery.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Exploring Proton-Only NMR Experiments and Filters for Daphnia In Vivo: Potential and Limitations

Environmental metabolomics provides insight into how anthropogenic activities have an impact on the health of an organism at the molecular level. Within this field, in vivo NMR stands out as a powerful tool for monitoring real-time changes in an organism's metabolome. Typically, these studies use 2D ¹³C-¹H experiments on ¹³C-enriched organisms. Daphnia are the most studied species, given their widespread use in toxicity testing. However, with COVID-19 and other geopolitical factors, the cost of isotope enrichment increased ~6-7 fold over the last two years, making ¹³C-enriched cultures difficult to maintain. Thus, it is essential to revisit proton-only in vivo NMR and ask, "Can any metabolic information be obtained from Daphnia using proton-only experiments?". Two samples are considered here: living and whole reswollen organisms. A range of filters are tested, including relaxation, lipid suppression, multiple-quantum, J-coupling suppression, 2D ¹H-¹H experiments, selective experiments, and those exploiting intermolecular single-quantum coherence. While most filters improve the ex vivo spectra, only the most complex filters succeed in vivo. If non-enriched organisms must be used, then, DREAMTIME is recommended for targeted monitoring, while IP-iSQC was the only experiment that allowed non-targeted metabolite identification in vivo. This paper is critically important as it documents not just the experiments that succeed in vivo but also those that fail and demonstrates first-hand the difficulties associated with proton-only in vivo NMR.

Benchtop NMR-Based Metabolomics: First Steps for Biomedical Application

Nuclear magnetic resonance (NMR)-based metabolomics is a valuable tool for identifying biomarkers and understanding the underlying metabolic changes associated with various diseases. However, the translation of metabolomics analysis to clinical practice has been limited by the high cost and large size of traditional high-resolution NMR spectrometers. Benchtop NMR, a compact and low-cost alternative, offers the potential to overcome these limitations and facilitate the wider use of NMR-based metabolomics in clinical settings. This review summarizes the current state of benchtop NMR for clinical applications where benchtop NMR has demonstrated the ability to reproducibly detect changes in metabolite levels associated with diseases such as type 2 diabetes and tuberculosis. Benchtop NMR has been used to identify metabolic biomarkers in a range of biofluids, including urine, blood plasma and saliva. However, further research is needed to optimize the use of benchtop NMR for clinical applications and to identify additional biomarkers that can be used to monitor and manage a range of diseases. Overall, benchtop NMR has the potential to revolutionize the way metabolomics is used in clinical practice, providing a more accessible and cost-effective way to study metabolism and identify biomarkers for disease diagnosis, prognosis, and treatment.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Metabolomic analyses redefine the biological classification of pancreatic cancer and correlate with clinical outcomes

Pancreatic ductal adenocarcinoma (PDAC) is characterized by high heterogeneity, and the postoperative prognosis of different patients often varies greatly. Therefore, the classification of pancreatic cancer patients and precise treatment becomes particularly important. In our study, 1 H NMR spectroscopy was used to analyze the 76 PDAC serum samples and identify the potential metabolic subtypes. The metabolic characteristics of each metabolic subtype were screened out and the relationship between metabolic subtype and the long-term prognosis was further identified. The clinical stages of PDAC did not show the metabolic differences at the serum metabolomic level. And three metabolic subtypes, basic, choline-like and amino acid-enriched types, were defined by the hierarchical cluster analysis of the serum metabolites and the disturbed metabolic pathways. The characteristic metabolites of each PDAC subtype were identified, and the metabolite model was established to distinguish the PDAC patients in the different subtypes. Among the three metabolic subtypes, choline-like type displayed better long-term prognosis compared to the other two types of patients. Metabolic subtypes are of clinical importance and are closer to expressing the heterogeneity in the actual life activities of pancreatic cancer than molecular typing. The excavation of metabolic subtypes based on this will be more in line with clinical reality and more promising to guide clinical precision individualization treatment.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

NMR-Based Metabolomics in Cancer Research

Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.

Neuroendocrine Neoplasms: Identification of Novel Metabolic Circuits of Potential Diagnostic Utility

The incidence of neuroendocrine neoplasms (NEN) is increasing, but established biomarkers have poor diagnostic and prognostic accuracy. Here, we aim to define the systemic metabolic consequences of NEN and to establish the diagnostic utility of proton nuclear magnetic resonance spectroscopy (¹H-NMR) for NEN in a prospective cohort of patients through a single-centre, prospective controlled observational study. Urine samples of 34 treatment-naïve NEN patients (median age: 59.3 years, range: 36-85): 18 had pancreatic (Pan) NEN, of which seven were functioning; 16 had small bowel (SB) NEN; 20 age- and sex-matched healthy control individuals were analysed using a 600 MHz Bruker ¹H-NMR spectrometer. Orthogonal partial-least-squares-discriminant analysis models were able to discriminate both PanNEN and SBNEN patients from healthy control (Healthy vs. PanNEN: AUC = 0.90, Healthy vs. SBNEN: AUC = 0.90). Secondary metabolites of tryptophan, such as trigonelline and a niacin-related metabolite were also identified to be universally decreased in NEN patients, while upstream metabolites, such as kynurenine, were elevated in SBNEN. Hippurate, a gut-derived metabolite, was reduced in all patients, whereas other gut microbial co-metabolites, trimethylamine-N-oxide, 4-hydroxyphenylacetate and phenylacetylglutamine, were elevated in those with SBNEN. These findings suggest the existence of a new systems-based neuroendocrine circuit, regulated in part by cancer metabolism, neuroendocrine signalling molecules and gut microbial co-metabolism. Metabonomic profiling of NEN has diagnostic potential and could be used for discovering biomarkers for these tumours. These preliminary data require confirmation in a larger cohort.

Sample Preparation and Data Analysis for NMR-Based Metabolomics

NMR spectroscopy has become one of the preferred analytical techniques for metabolomics studies due to its inherent nondestructive nature, ability to identify and quantify metabolites simultaneously in a complex mixture, minimal sample preparation requirement, and high degree of experimental reproducibility. NMR-based metabolomics studies involve the measurement and multivariate statistical analysis of metabolites present in biological samples such as biofluids, stool/feces, intestinal content, tissue, and cell extracts by high-resolution NMR spectroscopy-the goal then is to identify and quantify metabolites and evaluate changes of metabolite concentrations in response to some perturbation. Here we describe methodologies for NMR sample preparation of biofluids (serum, saliva, and urine) and stool/feces, intestinal content, and tissues for NMR experiments including extraction of polar metabolites and application of NMR in metabolomics studies. One dimensional (1D) ¹H NMR experiments with different variations such as pre-saturation, relaxation-edited, and diffusion-edited are routinely acquired for profiling and metabolite identification and quantification. 2D homonuclear ¹H-¹H TOCSY and COSY, 2D J-resolved, and heteronuclear ¹H-¹³C HSQC and HMBC are also performed to assist with metabolite identification and quantification. The NMR data are then subjected to targeted and/or untargeted multivariate statistical analysis for biomarker discovery, clinical diagnosis, toxicological studies, molecular phenotyping, and functional genomics.

Nuclear magnetic resonance-based tissue metabolomic analysis clarifies molecular mechanisms of gastric carcinogenesis

Gastric cancer (GC) is one of the deadliest cancers worldwide, and the progression of gastric carcinogenesis (GCG) covers multiple complicated pathological stages. Molecular mechanisms of GCG are still unclear. Here, we undertook NMR-based metabolomic analysis of aqueous metabolites extracted from gastric tissues in an established rat model of GCG. We showed that the metabolic profiles were clearly distinguished among 5 histologically classified groups: control, gastritis, low-grade gastric dysplasia, high-grade gastric dysplasia (HGD), and GC. Furthermore, we carried out metabolic pathway analysis based on identified significant metabolites and revealed significantly disturbed metabolic pathways closely associated with the 4 pathological stages, including oxidation stress, choline phosphorylation, amino acid metabolism, Krebs cycle, and glycolysis. Three metabolic pathways were continually disturbed during the progression of GCG, including taurine and hypotaurine metabolism, glutamine and glutamate metabolism, alanine, aspartate, and glutamate metabolism. Both the Krebs cycle and glycine, serine, and threonine metabolism were profoundly impaired in both the HGD and GC stages, potentially due to abnormal energy supply for tumor cell proliferation and growth. Furthermore, valine, leucine, and isoleucine biosynthesis and glycolysis were significantly disturbed in the GC stage for higher energy requirement of the rapid growth of tumor cells. Additionally, we identified potential gastric tissue biomarkers for metabolically discriminating the 4 pathological stages, which also showed good discriminant capabilities for their serum counterparts. This work sheds light on the molecular mechanisms of GCG and is of benefit to the exploration of potential biomarkers for clinically diagnosing and monitoring the progression of GCG.

Identifying unknown metabolites using NMR-based metabolic profiling techniques

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.

Plasma Metabolite Profiles in First Episode Psychosis: Exploring Symptoms Heterogeneity/Severity in Schizophrenia and Bipolar Disorder Cohorts

INTRODUCTION

The first symptoms of psychosis are frequently shared amongst several neuropsychiatry disorders, which makes the differentiation by clinical diagnosis challenging. Early recognition of symptoms is important in the management of psychosis. Therefore, the implementation of molecular biomarkers will be crucial for transforming the currently used diagnostic and therapeutic approach, improving insights into the underlying biological processes and clinical management.

OBJECTIVES

To define a set of metabolites that supports diagnosis or prognosis of schizophrenia (SCZ) and bipolar disorder (BD) at first onset psychosis.

METHODS

Plasma samples from 55 drug-naïve patients, 28 SCZ and 27 BD, and 42 healthy controls (HC). All participants underwent a seminaturalistic treatment regimen, clinically evaluated on a weekly basis until achieving clinical remission. All clinical or sociodemographic aspects considered for this study were equivalent between the groups at first-onset psychosis time point. The plasma samples were analyzed by untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) using reversed-phase and hydrophilic interaction chromatography. The acquired molecular features were analyzed with MetaboAnalyst.

RESULTS

We identified two patient groups with different metabolite profiles. Both groups are composed of SCZ and BD patients. We found differences between these two groups regarding general symptoms of PANSS score after remission (p = 0.008), and the improvement of general symptoms (delta of the score at remission minus the baseline) (-0.50 vs. -0.33, p = 0.019).

CONCLUSION

Our results suggest that plasma metabolite profiles cluster clinical remission phenotypes based on PANSS general psychopathology scores.

Analysis of NMR Metabolomics Data

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.

Metabolic adaptation to feed restriction on the green sturgeon (Acipenser medirostris) fingerlings

Food restriction may cause severe biological effects on wildlife and lead to population decline and extinction. The objective of the current study was to examine the metabolic effects on green sturgeon in response to feed restriction. Green sturgeon fingerlings were fed for two weeks at 12.5, 25, 50 and 100% of the optimum feeding rate (OFR), which corresponded to 0.25, 0.50, 1.00, and 2.00% body weight per day. We characterized the changes in hydrophilic and hydrophobic metabolites from extracts of muscle, liver, and kidney using nuclear magnetic resonance spectroscopy followed by multivariate statistical analysis. The results of principal component analysis (PCA) score plots from the analyses of hydrophilic metabolites showed that they exhibited a greater response to feed restriction than hydrophobic metabolites. In general, the hydrophilic metabolites in tissues from fish fed ≦25% of the OFR were separated from those fed 100% of the OFR in the PCA score plots. Among the three types of tissues examined, the overall metabolite changes showed a greater response to feed restriction in kidney tissue than in liver or muscle tissues. Numerous glucogenic amino acids in muscle and most amino acids in the kidney were decreased under feed restriction conditions. A significant decrease in ketone bodies (3-hydroxyisobutyrate) was observed in the muscle. Most fatty acids except for glycerol, phospholipid and cholesterol in the liver and kidney tissues were decreased under feed restriction conditions. Creatine phosphate, taurine and glycine were also significantly increased in tissues under feed restriction conditions. In conclusion, this study suggests that the manipulation of feed restriction under the current conditions perturbed metabolites related to energy metabolism, osmolality regulation, and antioxidation capacity in the sturgeon.

NMR Spectroscopy for Metabolomics Research

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

DQF J-RES NMR: Suppressing the singlet signals for improving the J-RES spectra from complex mixtures

Two-dimensional J-RESolved spectroscopy (J-RES) finds routine use in metabolomics for reducing signal overlap as it separates chemical shift and multiplet information along two frequency axes. However, only magnitude mode of the experiment is practical which prevents exploitation of its full resolving power. Tailing from high-intensity metabolite peaks often obscure nearby low-intensity metabolite peaks which leads to ambiguity in assignment of metabolites. Absorptive mode J-RES spectroscopy offers better-resolving power but comes at the cost of either sensitivity or complicated post-processing. Quite often for certain complex mixtures such as bio-fluids some components of the mixture display intense singlet signals which dominate the whole spectrum resulting in less reliable detection of weaker metabolite signals. Multi-frequency presaturation could suppress these intense singlets but will also remove the useful weaker multiplet peaks which are either totally eclipsed with the intense singlets or very close in frequency. We show that by using a double quantum filter (DQF) in magnitude mode J-RES technique, the intensity of the strong singlet metabolite peaks can be reduced relative to the intensity of the sparsely present multiplet metabolite signals. This approach leads to the identification of many weak intensity multiplet peaks which are otherwise undetected due to their overlap with intense singlet peaks in regular J-RES as well as 1D ¹H spectra. Although the improved intensity of most of the weaker peaks relative to the strong singlet peaks is observed, some multiplets can disappear due to the delay-dependent modulation of the signals by the DQF. A few DQF J-RES spectra recorded with different DQF delays, therefore, produce better assignment when analyzed together. The technique is demonstrated on a mixture of eight compounds, human urine, and plant extract samples.

NMR spectroscopy enables simultaneous quantification of carbohydrates for diagnosis of intestinal and gastric permeability

Increased intestinal or gastric permeability is one of the major hallmarks of liver cirrhosis. The current gold standard for diagnosis of aberrant gut permeability due to disease is the triple-sugar test, where carbohydrates are orally administered and urinary excretion is measured. Hereby, elevated lactulose levels indicate intestinal permeability, whereas increased sucrose levels reveal gastric permeability. However, reliable detection and quantification of these sugars in a complex biological fluid still remains challenging due to interfering substances. Here we used Nuclear Magnetic Resonance (NMR) spectroscopy with a simple and fast protocol, without any additional sample extraction steps, for straight-forward simultaneous quantification of sugars in urine in order to detect increased intestinal and gastric permeability. Collected urine samples were diluted in buffer and one- and two-dimensional proton spectra were recorded in order to reveal carbohydrate concentrations in individual urine samples containing mannitol, sucrose and/or lactulose. Overall, this article presents a fast and robust method for simultaneous quantification of different sugars down to low micro-molar concentrations for research studies and can be further extended for clinical studies with automation of the quantification process.

Current developments in homonuclear and heteronuclear J-resolved NMR experiments

Two-dimensional J-resolved (Jres) NMR experiments offer a simple, user-friendly spectral representation where the information of coupling constants and chemical shifts are separated into two orthogonal frequency axis. Since its initial proposal 40 years ago, Jres has been the focus of considerable interest both in improving the basic pulse sequence and in its successful application to a wide range of studies. Here, the latest developments in the design of novel Jres pulse schemes are reviewed, mainly focusing on obtaining pure absorption lineshapes, minimizing strong coupling artifacts, and also optimizing sensitivity and experimental measurements. A discussion of several Jres versions for the accurate measurement of a different number of homonuclear (J_HH ) and heteronuclear (J_CH ) coupling constants is presented, accompanied by some illustrative examples.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

Metabonomic alterations from pancreatic intraepithelial neoplasia to pancreatic ductal adenocarcinoma facilitate the identification of biomarkers in serum for early diagnosis of pancreatic cancer

Pancreatic cancer is a highly malignant disease with a poor prognosis and it is essential to diagnose and treat the disease at an early stage. The aim of this study was to understand the underlying biochemical mechanisms of pancreatic intraepithelial neoplasia (PanIN) and pancreatic ductal adenocarcinoma (PDAC) and to identify potential serum biomarkers for early detection of pancreatic cancer. 7,12-Dimethylbenz(a)anthracene (DMBA)-induced PanIN and PDAC rat models were established and the serum samples were collected. The serum samples were measured using (1)H nuclear magnetic resonance (NMR) spectroscopy and analyzed by chemometric methods including principal component analysis (PCA) and (orthogonal) partial least squares discriminant analysis ((O)PLS-DA). The related biochemical pathways were derived from KEGG analysis of the significantly different metabolites. As results, some serum metabolites demonstrated alarming metabolic changes in the precursor lesion of pancreatic cancer (PanIN-2 in this study). These changes involved elevated levels of ketone compounds including 3-hydroxybutyrate, acetoacetate, and acetone, some amino acids including asparagine, glutamate, threonine, and phenylalanine, glycoproteins and lipoproteins including N-acetylglycoprotein, LDL and VLDL, and some metabolites that have been shown to contribute to mutagenicity and cancer promotion such as deoxyguanosine and cytidine. More metabolites were shown to be significantly different between PanIN and PDAC, suggesting that a more complex set of changes occurs from noninvasive precursor lesion to invasive cancer. The serum metabonomic changes of rats with PanIN and PDAC may extend our understanding of pancreatic molecular pathogenesis, and the metabolic variations from PanIN to PDAC will be helpful to understand evolution processes of the pancreatic disease. NMR-based metabonomic analysis of animal models will be beneficial for the human study and will be helpful for the early detection of pancreatic cancer.

Detection of Lipid and Amphiphilic Biomarkers for Disease Diagnostics

Rapid diagnosis is crucial to effectively treating any disease. Biological markers, or biomarkers, have been widely used to diagnose a variety of infectious and non-infectious diseases. The detection of biomarkers in patient samples can also provide valuable information regarding progression and prognosis. Interestingly, many such biomarkers are composed of lipids, and are amphiphilic in biochemistry, which leads them to be often sequestered by host carriers. Such sequestration enhances the difficulty of developing sensitive and accurate sensors for these targets. Many of the physiologically relevant molecules involved in pathogenesis and disease are indeed amphiphilic. This chemical property is likely essential for their biological function, but also makes them challenging to detect and quantify in vitro. In order to understand pathogenesis and disease progression while developing effective diagnostics, it is important to account for the biochemistry of lipid and amphiphilic biomarkers when creating novel techniques for the quantitative measurement of these targets. Here, we review techniques and methods used to detect lipid and amphiphilic biomarkers associated with disease, as well as their feasibility for use as diagnostic targets, highlighting the significance of their biochemical properties in the design and execution of laboratory and diagnostic strategies. The biochemistry of biological molecules is clearly relevant to their physiological function, and calling out the need for consideration of this feature in their study, and use as vaccine, diagnostic and therapeutic targets is the overarching motivation for this review.

NMR window of molecular complexity showing homeostasis in superorganisms

NMR offers tremendous advantages in the analyses of molecular complexity. The “big-data” are produced during the acquisition of fingerprints that must be stored and shared for posterior analysis and verifications.

New Methodology for Known Metabolite Identification in Metabonomics/Metabolomics: Topological Metabolite Identification Carbon Efficiency (tMICE)

A new, simple-to-implement and quantitative approach to assessing the confidence in NMR-based identification of known metabolites is introduced. The approach is based on a topological analysis of metabolite identification information available from NMR spectroscopy studies and is a development of the metabolite identification carbon efficiency (MICE) method. New topological metabolite identification indices are introduced, analyzed, and proposed for general use, including topological metabolite identification carbon efficiency (tMICE). Because known metabolite identification is one of the key bottlenecks in either NMR-spectroscopy- or mass spectrometry-based metabonomics/metabolomics studies, and given the fact that there is no current consensus on how to assess metabolite identification confidence, it is hoped that these new approaches and the topological indices will find utility.

Analytical pitfalls and challenges in clinical metabolomics

Metabolomics-based strategies have become an integral part of modern clinical research, allowing for a better understanding of pathophysiological conditions and disease mechanisms, as well as providing innovative tools for more adequate diagnostic and prognosis approaches. Metabolomics is considered an essential tool in precision medicine, which aims for personalized prevention and tailor-made treatments. Nevertheless, multiple pitfalls may be encountered in clinical metabolomics during the entire workflow, hampering the quality of the data and, thus, the biological interpretation. This review describes the challenges underlying metabolomics-based experiments, discussing step by step the potential pitfalls of the analytical process, including study design, sample collection, storage, as well as preparation, chromatographic and electrophoretic separation, detection and data analysis. Moreover, it offers practical solutions and strategies to tackle these challenges, ensuring the generation of high-quality data.

Software-assisted serum metabolite quantification using NMR

The goal of metabolomics is to analyze a whole metabolome under a given set of conditions, and accurate and reliable quantitation of metabolites is crucial. Absolute concentration is more valuable than relative concentration; however, the most commonly used method in NMR-based serum metabolic profiling, bin-based and full data point peak quantification, provides relative concentration levels of metabolites and are not reliable when metabolite peaks overlap in a spectrum. In this study, we present the software-assisted serum metabolite quantification (SASMeQ) method, which allows us to identify and quantify metabolites in NMR spectra using Chenomx software. This software uses the ERETIC2 utility from TopSpin to add a digitally synthesized peak to a spectrum. The SASMeQ method will advance NMR-based serum metabolic profiling by providing an accurate and reliable method for absolute quantification that is superior to bin-based quantification.

A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments

Metabonomics/metabolomics is an important science for the understanding of biological systems and the prediction of their behaviour, through the profiling of metabolites. Two technologies are routinely used in order to analyse metabolite profiles in biological fluids: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), the latter typically with hyphenation to a chromatography system such as liquid chromatography (LC), in a configuration known as LC-MS. With both NMR and MS-based detection technologies, the identification of the metabolites in the biological sample remains a significant obstacle and bottleneck. This article provides guidance on methods for metabolite identification in biological fluids using NMR spectroscopy, and is illustrated with examples from recent studies on mice.

Network Analysis of Metabolite GWAS Hits: Implication of CPS1 and the Urea Cycle in Weight Maintenance

Background and Scope

Weight loss success is dependent on the ability to refrain from regaining the lost weight in time. This feature was shown to be largely variable among individuals, and these differences, with their underlying molecular processes, are diverse and not completely elucidated. Altered plasma metabolites concentration could partly explain weight loss maintenance mechanisms. In the present work, a systems biology approach has been applied to investigate the potential mechanisms involved in weight loss maintenance within the Diogenes weight-loss intervention study.

Methods and Results

A genome wide association study identified SNPs associated with plasma glycine levels within the CPS1 (Carbamoyl-Phosphate Synthase 1) gene (rs10206976, p-value = 4.709e-11 and rs12613336, p-value = 1.368e-08). Furthermore, gene expression in the adipose tissue showed that CPS1 expression levels were associated with successful weight maintenance and with several SNPs within CPS1 (cis-eQTL). In order to contextualize these results, a gene-metabolite interaction network of CPS1 and glycine has been built and analyzed, showing functional enrichment in genes involved in lipid metabolism and one carbon pool by folate pathways.

Conclusions

CPS1 is the rate-limiting enzyme for the urea cycle, catalyzing carbamoyl phosphate from ammonia and bicarbonate in the mitochondria. Glycine and CPS1 are connected through the one-carbon pool by the folate pathway and the urea cycle. Furthermore, glycine could be linked to metabolic health and insulin sensitivity through the betaine osmolyte. These considerations, and the results from the present study, highlight a possible role of CPS1 and related pathways in weight loss maintenance, suggesting that it might be partly genetically determined in humans.

Emerging new strategies for successful metabolite identification in metabolomics

This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties.

Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.

SpinCouple: Development of a Web Tool for Analyzing Metabolite Mixtures via Two-Dimensional J-Resolved NMR Database

A new Web-based tool, SpinCouple, which is based on the accumulation of a two-dimensional (2D) (1)H-(1)H J-resolved NMR database from 598 metabolite standards, has been developed. The spectra include both J-coupling and (1)H chemical shift information; those are applicable to a wide array of spectral annotation, especially for metabolic mixture samples that are difficult to label through the attachment of (13)C isotopes. In addition, the user-friendly application includes an absolute-quantitative analysis tool. Good agreement was obtained between known concentrations of 20-metabolite mixtures versus the calibration curve-based quantification results obtained from 2D-Jres spectra. We have examined the web tool availability using nine series of biological extracts, obtained from animal gut and waste treatment microbiota, fish, and plant tissues. This web-based tool is publicly available via http://emar.riken.jp/spincpl.

The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have evolved as the most common techniques in metabolomics studies, and each brings its own advantages and limitations. Unlike MS spectrometry, NMR spectroscopy is quantitative and does not require extra steps for sample preparation, such as separation or derivatization. Although the sensitivity of NMR spectroscopy has increased enormously and improvements continue to emerge steadily, this remains a weak point for NMR compared with MS. MS-based metabolomics provides an excellent approach that can offer a combined sensitivity and selectivity platform for metabolomics research. Moreover, different MS approaches such as different ionization techniques and mass analyzer technology can be used in order to increase the number of metabolites that can be detected. In this chapter, the advantages, limitations, strengths, and weaknesses of NMR and MS as tools applicable to metabolomics research are highlighted.

Use of nuclear magnetic resonance-based metabolomics to characterize the biochemical effects of naphthalene on various organs of tolerant mice

Naphthalene, the most common polycyclic aromatic hydrocarbon, causes airway epithelium injury in mice. Repeated exposure of mice to naphthalene induces airway epithelia that are resistant to further injury. Previous studies revealed that alterations in bioactivation enzymes and increased levels of gamma-glutamylcysteine synthase in the bronchioles protect tolerant mice from naphthalene and its reactive metabolites. In our current study, tolerance was induced in male ICR mice using a total of 7 daily intraperitoneal injections of naphthalene (200 mg/kg). Both naphthalene-tolerant and non-tolerant mice were challenged with a dose of 300 mg/kg naphthalene on day 8 to investigate metabolite differences. The lungs, liver, and kidneys were collected for histopathology 24 h after the challenge dose. Bronchial alveolar lavage fluid (BALF) and both hydrophilic and hydrophobic extracts from each organ were analyzed using nuclear magnetic resonance (NMR)-based metabolomics. The histological results showed no observable injuries to the airway epithelium of naphthalene-tolerant mice when compared with the control. In contrast, airway injuries were observed in mice given a single challenge dose (injury mice). The metabolomics analysis revealed that the energy metabolism in the lungs of tolerant and injury mice was significantly perturbed. However, antioxidant metabolites, such as glutathione and succinate, were significantly increased in the lungs of tolerant mice, suggesting a role for these compounds in the protection of organs from naphthalene-induced electrophilic metabolites and free radicals. Damage to the airway cellular membrane, as shown by histopathological results and increased acetone in the BALF and perturbation of hydrophobic lung extracts, including cholesterol, phosphorylcholine-containing lipids, and fatty acyl chains, were observed in injury mice. Consistent with our histopathological results, fewer metabolic effects were observed in the liver and kidney of mice after naphthalene treatments. In conclusion, NMR-based metabolomics reveals possible mechanisms of naphthalene tolerance and naphthalene-induced toxicity in the respiratory system of mice.

Pure shift NMR

Although scalar-coupling provides important structural information, the resulting signal splittings significantly reduce the resolution of NMR spectra. Limited resolution is a particular problem in proton NMR experiments, resulting in part from the limited proton chemical shift range (∼10 ppm) but even more from the splittings due to scalar coupling to nearby protons. "Pure shift" NMR spectroscopy (also known as broadband homonuclear decoupling) has been developed for disentangling overlapped proton NMR spectra. The resulting spectra are considerably simplified as they consist of single lines, reminiscent of proton-decoupled C-13 spectra at natural abundance, with no multiplet structure. The different approaches to obtaining pure shift spectra are reviewed here and several applications presented. Pure shift spectra are especially useful for highly overlapped proton spectra, as found for example in reaction mixtures, natural products and biomacromolecules.

Effect of the DGAT1 K232A genotype of dairy cows on the milk metabolome and proteome

Diglyceride O-acyltransferase 1 (DGAT1) is the enzyme that catalyzes the synthesis of triglycerides from diglycerides and acyl-coenzyme A. The DGAT1 K232A polymorphism was previously shown to have a significant influence on bovine milk production characteristics (milk yield, protein content, fat content, and fatty acid composition). The mechanism of this influence has, however, not been elucidated. In this study, metabolomics ((1)H-nuclear magnetic resonance) and proteomics (laser chromatography-tandem mass spectrometry) were applied to determine the serum and lipid metabolite composition and milk fat globule membrane proteome of milk samples from cows with the DGAT1 KK and AA genotypes. The milk samples from cows with the DGAT1 KK genotype contained more stomatin, sphingomyelin, choline, and carnitine, and less citrate, creatine or phosphocreatine, glycerol-phosphocholine, mannose-like sugar, acetyl sugar phosphate, uridine diphosphate (UDP)-related sugar, and orotic acid compared with milk samples from cows with the DGAT1 AA genotype. Based on these results, we propose that the differences between the DGAT1 genotypes may be related to stomatin-sphingomyelin lipid rafts as well as structural (cell membrane) differences in epithelial cells of the mammary gland. In conclusion, our study shows that, in addition to previously described changes in triglyceride composition, cows differing in DGAT1 polymorphism differ in their milk proteome and metabolome, which may help in further understanding the effect of the DGAT1 K232A polymorphism on milk production characteristics.

A new paradigm for known metabolite identification in metabonomics/metabolomics: metabolite identification efficiency

A new paradigm is proposed for assessing confidence in the identification of known metabolites in metabonomics studies using NMR spectroscopy approaches. This new paradigm is based upon the analysis of the amount of metabolite identification information retrieved from NMR spectra relative to the molecular size of the metabolite. Several new indices are proposed including: metabolite identification efficiency (MIE) and metabolite identification carbon efficiency (MICE), both of which can be easily calculated. These indices, together with some guidelines, can be used to provide a better indication of known metabolite identification confidence in metabonomics studies than existing methods. Since known metabolite identification in untargeted metabonomics studies is one of the key bottlenecks facing the science currently, it is hoped that these concepts based on molecular spectroscopic informatics, will find utility in the field.

Assigning regioisomeric or diastereoisomeric relations of problematic trisubstituted double-bonds through heteronuclear 2D selective J-resolved NMR spectroscopy

Although one of the first 2D NMR methods, but so far neglected, selectiveJ-resolved NMR spectroscopy offers a unique opportunity to help organic chemists in structure elucidation, avoiding natural and non-natural product misassignments.

NMR-based metabolomics: from sample preparation to applications in nutrition research

Metabolomics is the study of metabolites present in biological samples such as biofluids, tissue/cellular extracts and culture media. Measurement of these metabolites is achieved through use of analytical techniques such as NMR and mass spectrometry coupled to liquid chromatography. Combining metabolomic data with multivariate data analysis tools allows the elucidation of alterations in metabolic pathways under different physiological conditions. Applications of NMR-based metabolomics have grown in recent years and it is now widely used across a number of disciplines. The present review gives an overview of the developments in the key steps involved in an NMR-based metabolomics study. Furthermore, there will be a particular emphasis on the use of NMR-based metabolomics in nutrition research.

The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective

The ability to phenotype metabolic profiles in serum has increased substantially in recent years with the advent of metabolomics. Metabolomics is the study of the metabolome, defined as those molecules with an atomic mass less than 1.5 kDa. There are two main metabolomics methods: mass spectrometry (MS) and proton nuclear magnetic resonance (¹H NMR) spectroscopy, each with its respective benefits and limitations. MS has greater sensitivity and so can detect many more metabolites. However, its cost (especially when heavy labelled internal standards are required for absolute quantitation) and quality control is sub-optimal for large cohorts. ¹H NMR is less sensitive but sample preparation is generally faster and analysis times shorter, resulting in markedly lower analysis costs. ¹H NMR is robust, reproducible and can provide absolute quantitation of many metabolites. Of particular relevance to cardio-metabolic disease is the ability of ¹H NMR to provide detailed quantitative data on amino acids, fatty acids and other metabolites as well as lipoprotein subparticle concentrations and size. Early epidemiological studies suggest promise, however, this is an emerging field and more data is required before we can determine the clinical utility of these measures to improve disease prediction and treatment.

This review describes the theoretical basis of ¹H NMR; compares MS and ¹H NMR and provides a tabular overview of recent ¹H NMR-based research findings in the atherosclerosis field, describing the design and scope of studies conducted to date. ¹H NMR metabolomics-CVD related research is emerging, however further large, robustly conducted prospective, genetic and intervention studies are needed to advance research on CVD risk prediction and to identify causal pathways amenable to intervention.

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¹H NMR metabolomics is being increasingly applied to large cohort studies.

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Studies have identified potentially novel lipoprotein and metabolite predictors for CVD.

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Potential exists for the use of metabolomics in cardiovascular clinical practice.

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Current findings are too preliminary to translate into clinical recommendations.

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Further large scale studies are now needed to advance the field in a robust manner.

Emerging biomarkers and metabolomics for assessing toxic nephropathy and acute kidney injury (AKI) in neonatology

Identification of novel drug-induced toxic nephropathy and acute kidney injury (AKI) biomarkers has been designated as a top priority by the American Society of Nephrology. Increasing knowledge in the science of biology and medicine is leading to the discovery of still more new biomarkers and of their roles in molecular pathways triggered by physiological and pathological conditions. Concomitantly, the development of the so-called "omics" allows the progressive clinical utilization of a multitude of information, from those related to the human genome (genomics) and proteome (proteomics), including the emerging epigenomics, to those related to metabolites (metabolomics). In preterm newborns, one of the most important factors causing the pathogenesis and the progression of AKI is the interaction between the individual genetic code, the environment, the gestational age, and the disease. By analyzing a small urine sample, metabolomics allows to identify instantly any change in phenotype, including changes due to genetic modifications. The role of liquid chromatography-mass spectrometry (LC-MS), proton nuclear magnetic resonance (1H NMR), and other emerging technologies is strategic, contributing basically to the sudden development of new biochemical and molecular tests. Urine neutrophil gelatinase-associated lipocalin (uNGAL) and kidney injury molecule-1 (KIM-1) are closely correlated with the severity of kidney injury, representing noninvasive sensitive surrogate biomarkers for diagnosing, monitoring, and quantifying kidney damage. To become routine tests, uNGAL and KIM-1 should be carefully tested in multicenter clinical trials and should be measured in biological fluids by robust, standardized analytical methods.

Characterization of the biochemical effects of naphthalene on the mouse respiratory system using NMR-based metabolomics

Naphthalene is a ubiquitous environmental pollutant to which humans are exposed. Previous studies have demonstrated that naphthalene causes bronchiolar epithelial necrosis in the mouse distal airway, after parenteral administration. In this study, metabolic variations in the bronchoalveolar lavage fluid (BALF) and the lung tissues of naphthalene-treated mice and controls were examined using nuclear magnetic resonance (NMR)-based metabolomics to identify the toxic mechanism. Male ICR mice were treated with naphthalene [0, 50, 100 and 200 mg kg(-1), intraperitoneally (i.p.)]. After 24 h, BALF and lung tissues were collected and prepared for (1)H and J-resolved (JRES) NMR analysis after principal component analysis (PCA). PCA modeling of p-JRES spectra from the BALF, as well as hydrophilic and hydrophobic lung metabolites, enabled the high-dose group to be discriminated from the control group; increased levels of isopropanol, ethane, and acetone and lower levels of ethanol, acetate, formate, and glycerophosphocholine were detected in the BALF of mice treated with higher doses of naphthalene. Furthermore, increased isopropanol and phosphorylcholine-containing lipid levels and decreased succinate and glutamine levels were discovered in the lungs of naphthalene-exposed mice. These metabolic changes may be related to lipid peroxidation, disruptions of membrane components and imbalanced energy supply, and these results may partially explain the loss of cell membrane integrity in the airway epithelial cells of naphthalene-treated mice. We conclude that NMR-based metabolomic studies on BALF and lung tissues are a powerful tool to understand the mechanisms underlying respiratory toxicity.