Customized metabolomics database for the analysis of NMR ¹H-¹H TOCSY and ¹³C-¹H HSQC-TOCSY spectra of complex mixtures

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

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

MagMet: A fully automated web server for targeted nuclear magnetic resonance metabolomics of plasma and serum

Nuclear magnetic resonance (NMR) spectral analysis of biofluids can be a time-consuming process, requiring the expertise of a trained operator. With NMR becoming increasingly popular in the field of metabolomics, there is a growing need to change this paradigm and to automate the process. Here we introduce MagMet, an online web server, that automates the processing and quantification of 1D 1 H NMR spectra from biofluids-specifically, human serum/plasma metabolites, including those associated with inborn errors of metabolism (IEM). MagMet uses a highly efficient data processing procedure that performs automatic Fourier Transformation, phase correction, baseline optimization, chemical shift referencing, water signal removal, and peak picking/peak alignment. MagMet then uses the peak positions, linewidth information, and J-couplings from its own specially prepared standard metabolite reference spectral NMR library of 85 serum/plasma compounds to identify and quantify compounds from experimentally acquired NMR spectra of serum/plasma. MagMet employs linewidth adjustment for more consistent quantification of metabolites from higher field instruments and incorporates a highly efficient data processing procedure for more rapid and accurate detection and quantification of metabolites. This optimized algorithm allows the MagMet webserver to quickly detect and quantify 58 serum/plasma metabolites in 2.6 min per spectrum (when processing a dataset of 50-100 spectra). MagMet's performance was also assessed using spectra collected from defined mixtures (simulating other biofluids), with >100 previously measured plasma spectra, and from spiked serum/plasma samples simulating known IEMs. In all cases, MagMet performed with precision and accuracy matching the performance of human spectral profiling experts. MagMet is available at http://magmet.ca.

Fingerprinting and profiling in metabolomics of biosamples

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

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Metabolomics in Cell Biology

Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.

Problems, principles and progress in computational annotation of NMR metabolomics data

BACKGROUND

Compound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for ¹H 1-dimensional (¹H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards.

AIM OF REVIEW

This review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.

Novelty Detection for Metabolic Dynamics Established On Breast Cancer Tissue Using 2D NMR TOCSY Spectra

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Automatic novelty detection of metabolites of 2D-TOCSY NMR spectra.

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Metabolic profiling of the dynamics changes in Breast cancer tissue sample.

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Accurate and fast automatic multicomponent peak assignment of 2D NMR spectrum.

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One- and multi- novelty detection of metabolites.

Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research.

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.

Metabolomics on the study of marine organisms

BACKGROUND

Marine ecosystems are hosts to a vast array of organisms, being among the most richly biodiverse locations on the planet. The study of these ecosystems is very important, as they are not only a significant source of food for the world but also have, in recent years, become a prolific source of compounds with therapeutic potential. Studies of aspects of marine life have involved diverse fields of marine science, and the use of metabolomics as an experimental approach has increased in recent years. As part of the "omics" technologies, metabolomics has been used to deepen the understanding of interactions between marine organisms and their environment at a metabolic level and to discover new metabolites produced by these organisms.

AIM OF REVIEW

This review provides an overview of the use of metabolomics in the study of marine organisms. It also explores the use of metabolomics tools common to other fields such as plants and human metabolomics that could potentially contribute to marine organism studies. It deals with the entire process of a metabolomic study, from sample collection considerations, metabolite extraction, analytical techniques, and data analysis. It also includes an overview of recent applications of metabolomics in fields such as marine ecology and drug discovery and future perspectives of its use in the study of marine organisms.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review covers all the steps involved in metabolomic studies of marine organisms including, collection, extraction methods, analytical tools, statistical analysis, and dereplication. It aims to provide insight into all aspects that a newcomer to the field should consider when undertaking marine metabolomics.

It happened again: Convergent evolution of acylglucose specialized metabolism in black nightshade and wild tomato

Plants synthesize myriad phylogenetically restricted specialized (aka “secondary”) metabolites with diverse structures. Metabolism of acylated sugar esters in epidermal glandular secreting trichomes across the Solanaceae (nightshade) family is ideal for investigating the mechanisms of evolutionary metabolic diversification. We developed methods to structurally analyze acylhexose mixtures by 2D NMR, which led to the insight that the Old World species black nightshade (Solanum nigrum) accumulates acylglucoses and acylinositols in the same tissue. Detailed in vitro biochemistry, cross-validated by in vivo virus-induced gene silencing, revealed two unique features of the four-step acylglucose biosynthetic pathway: A trichome-expressed, neofunctionalized invertase-like enzyme, SnASFF1, converts BAHD-produced acylsucroses to acylglucoses, which, in turn, are substrates for the acylglucose acyltransferase, SnAGAT1. This biosynthetic pathway evolved independently from that recently described in the wild tomato Solanum pennellii, reinforcing that acylsugar biosynthesis is evolutionarily dynamic with independent examples of primary metabolic enzyme cooption and additional variation in BAHD acyltransferases.

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 serum and urine metabolomic profile reveals suppression of mitochondrial pathways in experimental sepsis-associated acute kidney injury

Sepsis-associated acute kidney injury (SA-AKI) is a significant problem in the critically ill that causes increased death. Emerging understanding of this disease implicates metabolic dysfunction in its pathophysiology. This study sought to identify specific metabolic pathways amenable to potential therapeutic intervention. Using a murine model of sepsis, blood and tissue samples were collected for assessment of systemic inflammation, kidney function, and renal injury. Nuclear magnetic resonance (NMR)-based metabolomics quantified dozens of metabolites in serum and urine that were subsequently submitted to pathway analysis. Kidney tissue gene expression analysis confirmed the implicated pathways. Septic mice had elevated circulating levels of inflammatory cytokines and increased levels of blood urea nitrogen and creatinine, indicating both systemic inflammation and poor kidney function. Renal tissue showed only mild histological evidence of injury in sepsis. NMR metabolomic analysis identified the involvement of mitochondrial pathways associated with branched-chain amino acid metabolism, fatty acid oxidation, and de novo NAD⁺ biosynthesis in SA-AKI. Renal cortical gene expression of enzymes associated with those pathways was predominantly suppressed. Renal cortical fatty acid oxidation rates were lower in septic mice with high inflammation, and this correlated with higher serum creatinine levels. Similar to humans, septic mice demonstrated renal dysfunction without significant tissue disruption, pointing to metabolic derangement as an important contributor to SA-AKI pathophysiology. Metabolism of branched-chain amino acid and fatty acids and NAD⁺ synthesis, which all center on mitochondrial function, appeared to be suppressed. Developing interventions to activate these pathways may provide new therapeutic opportunities for SA-AKI.NEW & NOTEWORTHY NMR-based metabolomics revealed disruptions in branched-chain amino acid metabolism, fatty acid oxidation, and NAD⁺ synthesis in sepsis-associated acute kidney injury. These pathways represent essential processes for energy provision in renal tubular epithelial cells and may represent targetable mechanisms for therapeutic intervention.

NMR-based metabolomics with enhanced sensitivity

NMR-based metabolomics, which emerged along with mass spectrometry techniques, is the preferred method for studying metabolites in medical research and food industries. However, NMR techniques suffer from inherently low sensitivity, regardless of their superior reproducibility. To overcome this, we made two beneficial modifications: we detuned the probe to reach a position called "Spin Noise Tuning Optimum" (SNTO), and we replaced the conventional cylindrical 5 mm NMR tube with an electric field component-optimized shaped tube. We found that concerted use of both modifications can increase the sensitivity (signal to noise ratio per unit volume) and detection of metabolites and decrease the measurement time by order of magnitude. In this study, we demonstrate and discuss the achieved signal enhancement of metabolites on model non-human (bovine serum, amino acid standard mixture) and human urine samples.

Evidence for Proline Utilization by Oral Bacterial Biofilms Grown in Saliva

Within the mouth bacteria are starved of saccharides as their main nutrient source between meals and it is unclear what drives their metabolism. Previously oral in vitro biofilms grown in saliva have shown proteolytic degradation of salivary proteins and increased extracellular proline. Although arginine and glucose have been shown before to have an effect on oral biofilm growth and activity, there is limited evidence for proline. Nuclear magnetic resonance (NMR) spectroscopy was used to identify extracellular metabolites produced by bacteria in oral biofilms grown on hydroxyapatite discs. Biofilms were inoculated with stimulated whole mouth saliva and then grown for 7 days using sterilized stimulated whole mouth saliva supplemented with proline, arginine or glucose as a growth-medium. Overall proline had a beneficial effect on biofilm growth-with significantly fewer dead bacteria present by biomass and surface area of the biofilms (p < 0.05). Where arginine and glucose significantly increased and decreased pH, respectively, the pH of proline supplemented biofilms remained neutral at pH 7.3-7.5. SDS-polyacrylamide gel electrophoresis of the spent saliva from proline and arginine supplemented biofilms showed inhibition of salivary protein degradation of immature biofilms. NMR analysis of the spent saliva revealed that proline supplemented biofilms were metabolically similar to unsupplemented biofilms, but these biofilms actively metabolized proline to 5-aminopentanoate, butyrate and propionate, and actively utilized glycine. This study shows that in a nutrient limited environment, proline has a beneficial effect on in vitro oral biofilms grown from a saliva inoculum.

Mitochondrial Regulation Assessment by 13C-NMR Isotopomer Analysis

Mitochondria play a central role in metabolic reprograming that occurs in numerous disease conditions. A precise evaluation of the extent of mitochondrial involvement in the metabolic alterations is essential for a better definition of metabolically based therapeutic strategies. In this chapter, some simple protocols are presented, using carbon 13 tracers and nuclear magnetic resonance isotopomer analysis, for the evaluation of mitochondrial contributions to intermediary metabolism and the metabolic effects of the implementation of some mitochondrial regulatory mechanisms.

Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.

NPid: an Automatic Approach to Rapid Identification of Known Natural Products in the Crude Extract of Crabapple Based on 2D 1H-13C Heteronuclear Correlation Spectra of the Extract Mixture

An automatic approach to identification of natural products (NPid) in complex extracts by exploring pure shift HSQC (psHSQC) and H2BC spectra of the mixture is developed, which integrated information on chemical shifts (CS), adjacent relationships (AR) and peak intensities (PI) of ¹H-¹³C groups for identification of candidate natural product in a customized NMR database. A weighted comprehensive score is calculated for each candidate from the values of CS, AR and PI to rate the likelihood of its existence in the complex mixture. Using the crude extract of crabapple (Malus fusca) as an example, a customized NMR database of natural products from plants of the genus Malus was constructed. The performance of NPid was first evaluated using simulated data in four scenarios, that is, for identification of structurally similar natural products, identification of natural products with part of peaks missing in psHSQC due to low concentration, without available adjacent relationship information, or without useful peak intensity information. The false positive and false negative rates of the natural products identified by NPid were estimated by Monte Carlo simulation. It shows that AR and PI can effectively reduce the false positive rate of identification. Proof of concept of the proposed method was elucidated on a model mixture consisting of 10 known natural products. Application of this method was then demonstrated on an authentic sample of crude extract of crabapple and 19 known natural products were successfully identified and confirmed by standard spiking.

Medium and long-term effects of low doses of Chlorpyrifos during the postnatal, preweaning developmental stage on sociability, dominance, gut microbiota and plasma metabolites

Autism spectrum disorder (ASD) is a complex neurodevelopmental pathology characterized by altered verbalizations, reduced social interaction behavior, and stereotypies. Environmental factors have been associated with its development. Some researchers have focused on pesticide exposure. Chlorpyrifos (CPF) is the most used Organophosphate. Previous developmental studies with CPF showed decreased, enhanced or no effect on social outcomes eminently in mice. The study of CPF exposure during preweaning stages on social behavior is sparse in mice and non-existent in rats. d stressors could be at the basis of ASD development, and around postnatal day 10 in the rat is equivalent to the human birthday in neurodevelopmental terms. We explored the effects of exposure to low doses (1mg/kg/mL/day) of CPF during this stage regarding: sociability, dominance gut microbiome and plasma metabolomic profile, since alterations in these systems have also been linked to ASD. There was a modest influence of CPF on social behavior in adulthood, with null effects during adolescence. Dominance and hierarchical status were not affected by exposure. Dominance status explained the significant reduction in reaction to social novelty observed on the sociability test. CPF induced a significant gut microbiome dysbiosis and triggered a hyperlipidemic, hypoglycemic/hypogluconeogenesis and a general altered cell energy production in females. These behavioral results in rats extend and complement previous studies with mice and show novel influences on gut metagenomics and plasma lipid profile and metabolomics, but do not stablish a relation between the exposure to CPF and the ASD phenotype. The effects of dominance status on reaction to social novelty have an important methodological meaning for future research on sociability.

Metabolomics in the Context of Plant Natural Products Research: From Sample Preparation to Metabolite Analysis

Plant-derived natural products have long been considered a valuable source of lead compounds for drug development. Natural extracts are usually composed of hundreds to thousands of metabolites, whereby the bioactivity of natural extracts can be represented by synergism between several metabolites. However, isolating every single compound from a natural extract is not always possible due to the complex chemistry and presence of most secondary metabolites at very low levels. Metabolomics has emerged in recent years as an indispensable tool for the analysis of thousands of metabolites from crude natural extracts, leading to a paradigm shift in natural products drug research. Analytical methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are used to comprehensively annotate the constituents of plant natural products for screening, drug discovery as well as for quality control purposes such as those required for phytomedicine. In this review, the current advancements in plant sample preparation, sample measurements, and data analysis are presented alongside a few case studies of the successful applications of these processes in plant natural product drug discovery.

Effect of a Shading Mesh on the Metabolic, Nutritional, and Defense Profiles of Harvested Greenhouse-Grown Organic Tomato Fruits and Leaves Revealed by NMR Metabolomics

Controlling the temperature inside a greenhouse during the summer is a problem of increasing importance in the Mediterranean countries, especially in the Spanish southeast. The metabolic profile of greenhouse tomatoes and leaves grown under conventional conditions and within the presence of a shade mesh (∼50% reduction of sunlight radiation) has been monitored. Tomatoes were weekly harvested from May to July 2017 and analyzed by NMR spectroscopy coupled to multivariate data analysis techniques, together with oxygen radical absorbance capacity (ORAC) assays (for antioxidant activity). Fatty acids and carotenoids profiles were unraveled by GC-FID and HPLC-DAD, respectively. To verify whether it would be possible to take advantage of different light growing conditions to potentiate a plant's defense system, leaves of the corresponding plants were collected and their methanolic extracts were analyzed by NMR toward deciphering new biomarkers, which were used to assess their antibacterial and antibiofilm activities. The presence of a shading mesh resulted in a reduction in tomato production and in smaller fruits with lower contents of sugars (glucose and fructose) and carotenoids (lycopene and β-carotene) and higher contents of organic acids, amino acids, and polyunsaturated fatty acids (linoleic and oleic acids) and of phenylpropanoids and flavonoids (which contributed to an increased antioxidant activity). Methanolic extracts of leaves of nonshaded plants showed a higher antibiofilm activity than that from shaded plants. This activity was well-correlated with an increase of phenolic compounds, together with some specific amino acids and organic acids from tomato leaves.

Influence of Drying Method on NMR-Based Metabolic Profiling of Human Cell Lines

Metabolic profiling of cell line and tissue extracts involves sample processing that includes a drying step prior to re-dissolving the cell or tissue extracts in a buffer for analysis by GC/LC-MS or NMR. Two of the most commonly used drying techniques are centrifugal evaporation under vacuum (SpeedVac) and lyophilization. Here, NMR spectroscopy was used to determine how the metabolic profiles of hydrophilic extracts of three human pancreatic cancer cell lines, MiaPaCa-2, Panc-1 and AsPC-1, were influenced by the choice of drying technique. In each of the three cell lines, 40-50 metabolites were identified as having statistically significant differences in abundance in redissolved extract samples depending on the drying technique used during sample preparation. In addition to these differences, some metabolites were only present in the lyophilized samples, for example, n-methyl-α-aminoisobutyric acid, n-methylnicotimamide, sarcosine and 3-hydroxyisovaleric acid, whereas some metabolites were only present in SpeedVac dried samples, for example, trimethylamine. This research demonstrates that the choice of drying technique used during the preparation of samples of human cell lines or tissue extracts can significantly influence the observed metabolome, making it important to carefully consider the selection of a drying method prior to preparation of such samples for metabolic profiling.

Quantitative Isotopomer Rates in Real-Time Metabolism of Cells Determined by NMR Methods

Tracer-based metabolism is becoming increasingly important for studying metabolic mechanisms in cells. NMR spectroscopy offers several approaches to measure label incorporation in metabolites, including ¹³ C- and ¹ H-detected spectra. The latter are generally more sensitive, but quantification depends on the proton-carbon ¹ J_CH coupling constant, which varies significantly between different metabolites. It is therefore not possible to have one experiment optimised for all metabolites, and quantification of ¹ H-edited spectra such as HSQCs requires precise knowledge of coupling constants. Increasing interest in tracer-based and metabolic flux analysis requires robust analyses with reasonably small acquisition times. Herein, we compare ¹³ C-filtered and ¹³ C-edited methods for quantification and show the applicability of the methods for real-time NMR spectroscopy of cancer-cell metabolism, in which label incorporations are subject to constant flux. We find an approach using a double filter to be most suitable and sufficiently robust to reliably obtain ¹³ C incorporations from difference spectra. This is demonstrated for JJN3 multiple myeloma cells processing glucose over 24 h. The proposed method is equally well suited for calculating the level of label incorporation in labelled cell extracts in the context of metabolic flux analysis.

Chemical Barcoding: A Nuclear-Magnetic-Resonance-Based Approach To Ensure the Quality and Safety of Natural Ingredients

One of the greatest challenges facing the functional food and natural health product (NHP) industries is sourcing high-quality, functional, natural ingredients for their finished products. Unfortunately, the lack of ingredient standards, modernized analytical methodologies, and industry oversight creates the potential for low quality and, in some cases, deliberate adulteration of ingredients. By exploring a diverse library of NHPs provided by the independent certification organization ISURA, we demonstrated that nuclear magnetic resonance (NMR) spectroscopy provides an innovative solution to authenticate botanicals and warrant the quality and safety of processed foods and manufactured functional ingredients. Two-dimensional NMR experiments were shown to be a robust and reproducible approach to capture the content of complex chemical mixtures, while a binary normalization step allows for emphasizing the chemical diversity in each sample, and unsupervised statistical methodologies provide key advantages to classify, authenticate, and highlight the potential presence of additives and adulterants.

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.

Identification of metabolites in coriander seeds (Coriandrum Sativum L.) aided by ultrahigh resolution total correlation NMR spectroscopy

NMR is a fast method for obtaining a holistic snapshot of the metabolome and also offers quantitative information without separating the compounds present in a complex mixture. Identification of the metabolites present in a plant extract sample is a crucial step for all plant metabolomics studies. In the present work, we used various two dimensional (2D) NMR methods such as J-resolved NMR, total correlation spectroscopy (TOCSY), and heteronuclear single quantum coherence sensitivity enhanced NMR spectroscopy for the identification of 36 common metabolites present in Coriandrum sativum L. seed extract. The identified metabolites belong to the following classes: organic acids, amino acids, and carbohydrates. ¹ H NMR spectra of such complex mixtures in general display tremendous signal overlap due to the presence of a large number of metabolites with closely resonating multiplet signals. This signal overlapping leads to ambiguity in an assignment, and hence, identification of metabolites becomes tedious or impossible in many cases. Therefore, the utility of pure-shift proton spectrum along the indirect (F₁ ) dimension of the F₁ -PSYCHE-TOCSY spectrum is demonstrated for overcoming ambiguity in assignment of metabolites in crowded spectral regions from Coriandrum sativum L. seed extract sample. Because pure-shift NMR methods yield ultrahigh resolution spectrum (i.e., a singlet peak per chemical site) along one or more dimensions, such spectra provide better identification of metabolites compared with regular 2D TOCSY where signal overlap and peak distortions lead to ambiguity in the assignment. Nine metabolites were unambiguously assigned by pure-shift F₁ -PSYCHE-TOCSY spectrum, which was unresolved in regular TOCSY spectrum.

Nuclear Magnetic Resonance Spectroscopy to Identify Metabolite Biomarkers of Nonresponsiveness to Targeted Therapy in Glioblastoma Tumor Stem Cells

Glioblastoma is the most common and malignant brain tumor, and current therapies confer only modest survival benefits. A major obstacle is our ability to monitor treatment effect on tumors. Current imaging modalities are ambiguous, and repeated biopsies are not encouraged. To scout for markers of treatment response, we used NMR spectroscopy to study the effects of a survivin inhibitor on the metabolome of primary glioblastoma cancer stem cells. Applying high resolution NMR spectroscopy (¹H resonance frequency: 800.03 MHz) to just 3 million cells per sample, we achieved sensitive and high resolving determinations of, e.g., amino acids, nucleosides, and constituents of the citric acid cycle. For control samples that were cultured, prepared, and measured at varying dates, peak area relative standard deviations were 15-20%. Analyses of unfractionated lysates were performed for straightforward compound identification with COLMAR and HMDB databases. Principal component analysis revealed that citrate levels were clearly upregulated in nonresponsive cells, while lactate levels substantially decreased following treatment for both responsive and nonresponsive cells. Hence, lactate and citrate may be potential markers of successful drug uptake and poor response to survivin inhibitors, respectively. Our metabolomics approach provided alternative biomarker candidates compared to spectrometry-based proteomics, underlining benefits of complementary methodologies. These initial findings make a foundation for exploring in vivo MR spectroscopy (MRS) of brain tumors, as citrate and lactate are MRS-visible. In sum, NMR metabolomics is a tool for addressing glioblastoma.

NMR Metabolomics as an Effective Tool To Unravel the Effect of Light Intensity and Temperature on the Composition of the Marine Microalgae Isochrysis galbana

NMR spectroscopy coupled with multivariate data analysis techniques was applied to obtain meaningful information about nontargeted metabolic changes on  Isochrysis galbana upon acclimation to different environmental conditions at indoor lab-scale. The effects of temperature (from 15 to 30 °C) and incident irradiance (from 250 to 1600 μmol m^-2 s^-1) at a constant dilution rate of 0.3 h^-1 were evaluated. High irradiances stimulated a decrease of chlorophyll a, fucoxanthin and amino acids content, and the conversion of polar fatty acids (PLs, GLs, DGDGs, SGDGs) to neutral fatty acids (saturated and unsaturated). High temperatures together with high irradiances decreased PUFAs concentration, including omega-3 fatty acids. Under low irradiance and temperature organic osmolytes (homarine, DMSP, GBT, and glycerol), and sugars (glucose, trehalose, and galactose) were also reduced.

ASAP-HSQC-TOCSY for fast spin system identification and extraction of long-range couplings

Based on Ernst-angle-type excitation and Acceleration by Sharing Adjacent Polarization (ASAP), a fast HSQC-TOCSY experiment is introduced. In the approach, the DIPSI-2 isotropic mixing period of the ASAP-HSQC is simply shifted, which provides a TOCSY period without additional application of rf-energy. The ASAP-HSQC-TOCSY allows the acquisition of a conventional 2D in about 30 s. Alternatively, it allows the acquisition of highly carbon-resolved spectra (several Hz digital resolution) on the order of minutes. An ASAP-HSQC-TOCSY-IPAP variant, finally, allows the sign-sensitive extraction of heteronuclear long-range coupling constants from a pair of highly resolved spectra in less than an hour. Pulse sequences, several example spectra, and a discussion of results are given.

A framework for tracer-based metabolism in mammalian cells by NMR

Metabolism changes extensively during the normal proliferation and differentiation of mammalian cells, and in cancer and inflammatory diseases. Since changes in the metabolic network reflect interactions between genetic, epigenetic and environmental changes, it is helpful to study the flow of label from isotopically labelled precursors into other metabolites rather than static metabolite levels. For this Nuclear Magnetic Resonance (NMR) spectroscopy is an attractive technique as it can quantify site-specific label incorporation. However, for applications using human cells and cell lines, the challenge is to optimize the process to maximize sensitivity and reproducibility. Here we present a new framework to analyze metabolism in mammalian cell lines and primary cells, covering the workflow from the preparation of cells to the acquisition and analysis of NMR spectra. We have applied this new approach in hematological and liver cancer cell lines and confirm the feasibility of tracer-based metabolism in primary liver cells.

RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies

INTRODUCTION

The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The "putatively annotated compound" category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of "putatively annotated compound" assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data.

OBJECTIVES

To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to "putatively annotated compound" assignments in NMR-based metabolic profiling studies.

METHODS

The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment.

RESULTS

Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to "putatively annotated compounds" in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories.

CONCLUSION

RANCM is intended to reduce the ambiguity in "putatively annotated compound" assignments, to facilitate effective communication of the degree of confidence in "putatively annotated compound" assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.

Automated Tools for the Analysis of 1D-NMR and 2D-NMR Spectra

Nuclear magnetic resonance (NMR) spectroscopy is becoming increasingly automated. Most modern NMR spectrometers are now equipped with auto-tune/auto-match probes along with automated locking and shimming systems. Likewise, more and more instruments, especially for NMR-based metabolomics applications, are equipped with automated sample changers. All this instrumental automation allows NMR data to be collected at a rate of >100 samples/day. However, a continuing bottleneck in NMR-based metabolomics has been the time required to manually analyze and annotate the collected NMR spectra. In many cases, manual spectral annotation and analysis can take one or more hours per spectrum. Fortunately, over the past few years, several software tools have been developed that largely automate the spectral deconvolution or spectral annotation process. Using these tools requires that the samples must be prepared and the NMR spectra must be acquired in a very specific manner. In this chapter, we will describe the step-by-step preparation of biofluid samples along with the required protocols for acquiring optimal spectra for automated NMR metabolomics analysis. We will also discuss the use of three common tools (Chenomx NMR Suite, Bayesil, and COLMARm) for (semi-) automated profiling, and annotation of 1D- and 2D-NMR spectra of biofluids.

Multiplicity-edited 1 H-1 H TOCSY experiment

A ¹ H-¹ H total correlation spectroscopy (TOCSY) experiment incorporating ¹³ C multiplicity information is proposed. In addition, broadband ¹ H homodecoupling in the indirect dimension can be implemented using a perfect BIRD module that affords exclusive ¹ H chemical shift evolution with full decoupling of all heteronuclear and homonuclear (including ² J_HH ) coupling constants. As a complement to the normal TOCSY and the recent PSYCHE-TOCSY experiments, this novel multiplicity-edited TOCSY experiment distinguishes between CH/CH₃ (phased up) and CH₂ (phased down) cross-peaks, which facilitates resonance analysis and assignment.

Non-Uniform and Absolute Minimal Sampling for High-Throughput Multidimensional NMR Applications

Many biomolecular NMR applications can benefit from the faster acquisition of multidimensional NMR data with high resolution and their automated analysis and interpretation. In recent years, a number of non-uniform sampling (NUS) approaches have been introduced for the reconstruction of multidimensional NMR spectra, such as compressed sensing, thereby bypassing traditional Fourier-transform processing. Such approaches are applicable to both biomacromolecules and small molecules and their complex mixtures and can be combined with homonuclear decoupling (pure shift) and covariance processing. For homonuclear 2D TOCSY experiments, absolute minimal sampling (AMS) permits the drastic shortening of measurement times necessary for high-throughput applications for identification and quantification of components in complex biological mixtures in the field of metabolomics. Such TOCSY spectra can be comprehensively represented by graphic theoretical maximal cliques for the identification of entire spin systems and their subsequent query against NMR databases. Integration of these methods in webservers permits the rapid and reliable identification of mixture components. Recent progress is reviewed in this Minireview.

NMR-Based Metabolomics Approach To Study the Influence of Different Conditions of Water Irrigation and Greenhouse Ventilation on Zucchini Crops

This study describes the approach of ¹H NMR metabolomic profiling for the differentiation of zucchini produced under different conditions of water irrigation (desalinated seawater -0.397 dS/m, 0.52 €/m³ vs groundwater -2.36 dS/m, 0.29 €/m³) and ventilation (surface area of the vent openings/greenhouse area was 15.0% for one sector and 9.8% for the other). Overall, 72 extracts of zucchini ( Cucubirta pepo L. cv Victoria) under four different conditions were regularly analyzed during the spring-summer cycle from April to July 2017. We have found that zucchini plants irrigated with desalinated seawater increased the zucchini production yield, presented fruits with higher concentration of glucose, fructose, and vitamin B3, and displayed an increased antioxidant activity. On the contrary, plant groundwater irrigation produced the increment of sucrose level that could rise the sweetness perception of the fruits. Finally, the ventilation variable produced a higher concentration of trigonelline, histidine, and phenylalanine but only on those zucchinis irrigated with groundwater.

NMR-based metabolic toxicity of low-level Hg exposure to earthworms

Mercury is a globally distributed toxicant to aquatic animals and mammals. However, the potential risks of environmental relevant mercury in terrestrial systems remain largely unclear. The metabolic profiles of the earthworm Eisenia fetida after exposure to soil contaminated with mercury at 0.77 ± 0.09 mg/kg for 2 weeks were investigated using a two-dimensional nuclear magnetic resonance-based (¹H-¹³C NMR) metabolomics approach. The results revealed that traditional endpoints (e.g., mortality and weight loss) did not differ significantly after exposure. Although histological examination showed sub-lethal toxicity in the intestine as a result of soil ingestion, the underlying mechanisms were unclear. Metabolite profiles revealed significant decreases in glutamine and 2-hexyl-5-ethyl-3-furansulfonate in the exposed group and remarkable increases in glycine, alanine, glutamate, scyllo-inositol, t-methylhistidine and myo-inositol. More importantly, metabolic network analysis revealed that low mercury in the soil disrupted osmoregulation, amino acid and energy metabolisms in earthworms. A metabolic net link and schematic diagram of mercury-induced responses were proposed to predict earthworm responses after exposure to mercury at environmental relevant concentrations. These results improved the current understanding of the potential toxicity of low mercury in terrestrial systems.

NMR-based metabolomics of biofluids in cancer

This review describes the current status of NMR-based metabolomics of biofluids with respect to cancer risk assessment, detection, disease characterization, prognosis, and treatment monitoring. While the metabolism of cancer cells is altered compared with that of non-proliferating cells, the metabolome of blood and urine reflects the entire organism. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, but translation to clinical practice is currently hindered by lack of validation, difficulties in biological interpretation, and non-standardized analytical procedures.

Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods

Metabolomics has made significant progress in multiple fronts in the last 18 months. This minireview aimed to give an overview of these advancements in the light of their contribution to targeted and untargeted metabolomics. New computational approaches have emerged to overcome the manual absolute quantitation step of metabolites in one-dimensional (1D) ¹H nuclear magnetic resonance (NMR) spectra. This provides more consistency between inter-laboratory comparisons. Integration of two-dimensional (2D) NMR metabolomics databases under a unified web server allowed for very accurate identification of the metabolites that have been catalogued in these databases. For the remaining uncatalogued and unknown metabolites, new cheminformatics approaches have been developed by combining NMR and mass spectrometry (MS). These hybrid MS/NMR approaches accelerated the identification of unknowns in untargeted studies, and now they are allowing for profiling ever larger number of metabolites in application studies.

(3, 2)D 1H, 13C BIRDr,X-HSQC-TOCSY for NMR structure elucidation of mixtures: application to complex carbohydrates

Overlap of NMR signals is the major cause of difficulties associated with NMR structure elucidation of molecules contained in complex mixtures. A 2D homonuclear correlation spectroscopy in particular suffers from low dispersion of ¹H chemical shifts; larger dispersion of ¹³C chemical shifts is often used to reduce this overlap, while still providing the proton-proton correlation information e.g. in the form of a 2D ¹H, ¹³C HSQC-TOCSY experiment. For this methodology to work, ¹³C chemical shift must be resolved. In case of ¹³C chemical shifts overlap, ¹H chemical shifts can be used to achieve the desired resolution. The proposed (3, 2)D ¹H, ¹³C BIRD^r,X-HSQC-TOCSY experiment achieves this while preserving singlet character of cross peaks in the F₁ dimension. The required high-resolution in the ¹³C dimension is thus retained, while the cross peak overlap occurring in a regular HSQC-TOCSY experiment is eliminated. The method is illustrated on the analysis of a complex carbohydrate mixture obtained by depolymerisation of a fucosylated chondroitin sulfate isolated from the body wall of the sea cucumber Holothuria forskali.

Metabolomics: State-of-the-Art Technologies and Applications on Drosophila melanogaster

Metabolomics is one of the latest "omics" technology concerned with the high-throughput identification and quantification of metabolites, the final products of cellular processes. The revealed data provide an instantaneous snapshot of an organism's metabolic pathways, which can be used to explain its phenotype or physiology. On the other hand, Drosophila has shown its power in studying metabolism and related diseases. At this stage, we have the state-of-the-art knowledge in place: a potential candidate to study cellular metabolism (Drosophila melanogaster) and a powerful methodology for metabolic network decipherer (metabolomics). Yet missing is advanced metabolomics technologies like isotope-assisted metabolomics optimized for Drosophila. In this chapter, we will discuss on the current status and future perspectives in technologies and applications of Drosophila metabolomics.

SOFAST-HMQC-an efficient tool for metabolomics

Nuclear magnetic resonance (NMR)-based metabolomics relies mostly on 1D NMR; however, the technique is limited by overlap of the signals from the metabolites. In order to circumvent this problem, 2D ¹H-¹³C correlation spectroscopy techniques are often used. However owing to poorer natural abundance and gyromagnetic ratio of ¹³C, the acquisition time for 2D ¹H-¹³C heteronuclear single quantum coherence spectroscopy (HSQC) is long. This makes it almost impossible to be used in high throughput study. We have reported the application of selective optimized flip angle short transient (SOFAST) technique coupled to heteronuclear multiple quantum correlation (HMQC) along with nonlinear sampling (NUS) in urine and serum samples. This technique takes sevenfold less experimental time than the conventional ¹H-¹³C HSQC experiment with retention of almost all molecular information. Hence, this can be used for high throughput study. Graphical abstract SOFAST-HMQC is a two-dimensional NMR technique that significantly decreases experimental time without loss of information. This technique is applied in complex biofluid samples that are used for high throughput metabolomics studies and shows promise of better information recovery than conventional two-dimensional NMR technique in shorter time.

Accurate Identification of Unknown and Known Metabolic Mixture Components by Combining 3D NMR with Fourier Transform Ion Cyclotron Resonance Tandem Mass Spectrometry

Metabolite identification in metabolomics samples is a key step that critically impacts downstream analysis. We recently introduced the SUMMIT NMR/mass spectrometry (MS) hybrid approach for the identification of the molecular structure of unknown metabolites based on the combination of NMR, MS, and combinatorial cheminformatics. Here, we demonstrate the feasibility of the approach for an untargeted analysis of both a model mixture and E. coli cell lysate based on 2D/3D NMR experiments in combination with Fourier transform ion cyclotron resonance MS and MS/MS data. For 19 of the 25 model metabolites, SUMMIT yielded complete structures that matched those in the mixture independent of database information. Of those, seven top-ranked structures matched those in the mixture, and four of those were further validated by positive ion MS/MS. For five metabolites, not part of the 19 metabolites, correct molecular structural motifs could be identified. For E. coli, SUMMIT MS/NMR identified 20 previously known metabolites with three or more ¹H spins independent of database information. Moreover, for 15 unknown metabolites, molecular structural fragments were determined consistent with their spin systems and chemical shifts. By providing structural information for entire metabolites or molecular fragments, SUMMIT MS/NMR greatly assists the targeted or untargeted analysis of complex mixtures of unknown compounds.

Fast 2D NMR Spectroscopy for In vivo Monitoring of Bacterial Metabolism in Complex Mixtures

The biological toolbox is full of techniques developed originally for analytical chemistry. Among them, spectroscopic experiments are very important source of atomic-level structural information. Nuclear magnetic resonance (NMR) spectroscopy, although very advanced in chemical and biophysical applications, has been used in microbiology only in a limited manner. So far, mostly one-dimensional ¹H experiments have been reported in studies of bacterial metabolism monitored in situ. However, low spectral resolution and limited information on molecular topology limits the usability of these methods. These problems are particularly evident in the case of complex mixtures, where spectral peaks originating from many compounds overlap and make the interpretation of changes in a spectrum difficult or even impossible. Often a suite of two-dimensional (2D) NMR experiments is used to improve resolution and extract structural information from internuclear correlations. However, for dynamically changing sample, like bacterial culture, the time-consuming sampling of so-called indirect time dimensions in 2D experiments is inefficient. Here, we propose the technique known from analytical chemistry and structural biology of proteins, i.e., time-resolved non-uniform sampling. The method allows application of 2D (and multi-D) experiments in the case of quickly varying samples. The indirect dimension here is sparsely sampled resulting in significant reduction of experimental time. Compared to conventional approach based on a series of 1D measurements, this method provides extraordinary resolution and is a real-time approach to process monitoring. In this study, we demonstrate the usability of the method on a sample of Escherichia coli culture affected by ampicillin and on a sample of Propionibacterium acnes, an acne causing bacterium, mixed with a dose of face tonic, which is a complicated, multi-component mixture providing complex NMR spectrum. Through our experiments we determine the exact concentration and time at which the anti-bacterial agents affect the bacterial metabolism. We show, that it is worth to extend the NMR toolbox for microbiology by including techniques of 2D z-TOCSY, for total "fingerprinting" of a sample and 2D ¹³C-edited HSQC to monitor changes in concentration of metabolites in selected metabolic pathways.

Maximal clique method for the automated analysis of NMR TOCSY spectra of complex mixtures

Characterization of the chemical components of complex mixtures in solution is important in many areas of biochemistry and chemical biology, including metabolomics. The use of 2D NMR total correlation spectroscopy (TOCSY) experiments has proven very useful for the identification of known metabolites as well as for the characterization of metabolites that are unknown by taking advantage of the good resolution and high sensitivity of this homonuclear experiment. Due to the complexity of the resulting spectra, automation is critical to facilitate and speed-up their analysis and enable high-throughput applications. To better meet these emerging needs, an automated spin-system identification algorithm of TOCSY spectra is introduced that represents the cross-peaks and their connectivities as a mathematical graph, for which all subgraphs are determined that are maximal cliques. Each maximal clique can be assigned to an individual spin system thereby providing a robust deconvolution of the original spectrum for the easy extraction of critical spin system information. The approach is demonstrated for a complex metabolite mixture consisting of 20 compounds and for E. coli cell lysate.

Fast profiling of metabolite mixtures using chemometric analysis of a speeded-up 2D heteronuclear correlation NMR experiment

One-dimensional (1D) NMR spectra of mixtures of metabolites suffer from severe overlap of spectral resonances and hence recent research in NMR-based metabolomics focuses on using two-dimensional (2D) NMR experiments for metabolite fingerprinting.

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.

Challenges and perspectives in quantitative NMR

This perspective article summarizes, from the author's point of view at the beginning of 2016, the major challenges and perspectives in the field of quantitative NMR. The key concepts in quantitative NMR are first summarized; then, the most recent evolutions in terms of resolution and sensitivity are discussed, as well as some potential future research directions in this field. A particular focus is made on methodologies capable of boosting the resolution and sensitivity of quantitative NMR, which could open application perspectives in fields where the sample complexity and the analyte concentrations are particularly challenging. These include multi-dimensional quantitative NMR and hyperpolarization techniques such as para-hydrogen-induced polarization or dynamic nuclear polarization. Because quantitative NMR cannot be dissociated from the key concepts of analytical chemistry, i.e. trueness and precision, the methodological developments are systematically described together with their level of analytical performance. Copyright © 2016 John Wiley & Sons, Ltd.

Comprehensive Metabolite Identification Strategy Using Multiple Two-Dimensional NMR Spectra of a Complex Mixture Implemented in the COLMARm Web Server

Identification of metabolites in complex mixtures represents a key step in metabolomics. A new strategy is introduced, which is implemented in a new public web server, COLMARm, that permits the coanalysis of up to three two-dimensional (2D) NMR spectra, namely, ¹³C-¹H HSQC (heteronuclear single quantum coherence spectroscopy), ¹H-¹H TOCSY (total correlation spectroscopy), and ¹³C-¹H HSQC-TOCSY, for the comprehensive, accurate, and efficient performance of this task. The highly versatile and interactive nature of COLMARm permits its application to a wide range of metabolomics samples independent of the magnetic field. Database query is performed using the HSQC spectrum, and the top metabolite hits are then validated against the TOCSY-type experiment(s) by superimposing the expected cross-peaks on the mixture spectrum. In this way the user can directly accept or reject candidate metabolites by taking advantage of the complementary spectral information offered by these experiments and their different sensitivities. The power of COLMARm is demonstrated for a human serum sample uncovering the existence of 14 metabolites that hitherto were not identified by NMR.