Unified and isomer-specific NMR metabolomics database for the accurate analysis of (13)C-(1)H HSQC spectra

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

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

Metabolic flexibility associated with flight time among combat pilots of the Brazilian air force

INTRODUCTION

Fighter pilots must support the effects of many stressors, including physical and psychological exertion, circadian disturbance, jet lag, and environmental stress. Despite the rigorous selection of military pilots, those factors predispose to failures in physiological compensatory mechanisms and metabolic flexibility.

OBJECTIVES

We compared through NMR-based metabolomics the metabolic profile of Brazilian F5 fighter pilots with different flight experiences vs. the control group of non-pilots. We hypothesized that combat pilots have metabolic flexibility associated with combat flight time.

METHODS

We evaluated for the first time 34 Brazilian fighter pilots from Santa Cruz Air Base (Rio de Janeiro, RJ) allocated into three groups: pilots with lower total accumulated flight experience < 1,100 h (PC1, n = 7); pilots with higher total accumulated flight experience ≥ 1,100 h (PC2, n = 6); military non-pilots (CONT, n = 21). Data collection included anthropometric measurements, total blood count, lipidogram, markers of oxidative stress, and serum NMR-based metabolomics.

RESULTS

In comparison with controls (p < 0.05), pilots exhibited decreased levels of white blood cells (-13%), neutrophils (-15%), lymphocytes (-20%), alfa-glucose (-13%), lactate (-26%), glutamine (-11%), histidine (-20%), and tyrosine (-11%), but higher isobutyrate (+ 10%) concentrations. Significant correlations were found between lactate vs. amino acids in CONT (r = 0.55-0.68, p < 0.001), and vs. glutamine in PC2 (r = 0.94, p = 0.01).

CONCLUSION

Fighter pilots with lower experience showed a dysregulation in immune-metabolic function in comparison with controls, which seemed to be counteracted by the accumulation of flight hours. Those findings might have implications for the health preservation and operational training of fighter pilots.

Changes in primary metabolite content may affect thrips feeding preference in soybean crops

Past research has characterized the induction of plant defenses in response to chewing insect damage. However, little is known about plant responses to piercing-sucking insects that feed on plant cell-contents like thrips (Caliothrips phaseoli). In this study, we used NMR spectroscopy to measure metabolite changes in response to six days of thrips damage from two field-grown soybean cultivars (cv.), known for their different susceptibility to Caliothrips phaseoli. We observed that thrips damage reduces sucrose concentration in both cultivars, while pinitol, the most abundant leaf soluble carbohydrate, is induced in cv. Charata but not in cv. Williams. Thrips did not show preference for leaves where sucrose or pinitol were externally added, at tested concentration. In addition, we also noted that cv. Charata was less naturally colonized and contained higher levels of trigonelline, tyrosine as well as several compounds that we have not yet identified. We have established that preference-feeding clues are not dependent on the plants major soluble carbohydrates but may depend on other types of compounds or leaf physical characteristics.

Identification of variety-specific metabolites of basil by high performance thin layer chromatography-assisted metabolic profiling techniques

The technological advances of analytical instrumentation and techniques has laid the ground for the rapid expansion of metabolomics or in a wider sense, untargeted analysis applied to life sciences themes. However, the objective of identifying all existing metabolites within organisms remains a daunting challenge. All analytical techniques exhibit varying degrees of sensitivity and versatility for the detection of metabolites and none of the existing analytical platforms can be expected to be ideal for exhaustive chemical profiling. Planar liquid chromatography, and in particular, high performance thin layer chromatography (HPTLC), has been used for chemical profiling of natural products in conjunction with metabolomics. HPTLC has specific advantages which include its ability to generate reliable chemical fingerprinting data and facilitate preparative work for metabolite isolation during later stages of metabolomics analysis. In this study, we investigated the chemical profiles of four commercially available basil cultivars, namely Dolly, Emily, Keira, and Rosie. We used HPTLC as the primary analytical tool for the separation of basil cultivars based on detected metabolites, and then compared the results with those obtained with other analytical platforms. We identified the characteristic marker compounds of each basil cultivar from the HPTLC plates and validated their potential using LC-MS and GC-MS analyses as a metabolomics tool. Firstly, we compared the HPTLC data of the four cultivars, obtained with two systems that used silica gel 60 and two mobile phases composed of toluene-EtOAc (8:2, v/v) and EtOAc-formic acid-acetic acid-water (100:11:11:27, v/v), with 1H NMR data to evaluate their separation power. Despite providing lower resolution and detecting fewer compounds, the HPTLC separation power was comparable, and in some cases even better than that of 1H NMR. Additionally, we investigated the potential of HPTLC as a tool for chemical fingerprinting and demonstrated its suitability for preparative purposes that are essential for identifying metabolites in mixture analysis. Metabolites were easily isolated from sample mixtures, and identified with the assistance of GC-MS, LC-MS, and TLC-densitometry.. Several marker compounds were thus identified, including 2,4 di-tertbutylphenol, palmitic acid, hexadecanamide, 9-octadecenamide, squalene, hentriacontane, methyl 3-(3,5-ditert‑butyl‑4-hydroxyphenyl)propanoic acid, sagerinic acid, and cyanidin-3-O-sophoroside.

Glucose metabolism sustains heme-induced Trypanosoma cruzi epimastigote growth in vitro

Chagas disease is caused by the protozoan parasite, Trypanosoma cruzi. This parasite alternates between an insect vector and a mammalian host. T. cruzi epimastigotes reside in the insect vector and coexist with the blood components of the vertebrate host. The metabolic profile of T. cruzi has been extensively studied; however, changes in its metabolism in response to signaling molecules present in the vector are poorly understood. Heme acts as a physiological oxidant that triggers intense epimastigote proliferation and upregulates the expression of genes related to glycolysis and aerobic fermentation in vitro. Here, heme-cultured epimastigotes increased D-glucose consumption. In fact, heme-cultured parasites secreted more succinate (the end product of the so-called succinic fermentation) followed by glucose intake. Increased succinate levels reduced the extracellular pH, leading to acidification of the supernatant. However, the acidification and proliferation stimulated by heme was impaired when glycolysis was inhibited. Otherwise, when glucose amount is enhanced in supernatant, heme-cultured parasites increased its growth whereas the glucose depletion caused a delay in proliferation. Heme supplementation increased epimastigote electron transport system-related O2 consumption rates, while glucose addition reduced both the electron transport system-related O2 consumption rates and spare respiratory capacity, indicating a Crabtree-like effect. These results show that glycolysis predominated in heme-cultured epimastigotes over oxidative phosphorylation for energy supply when glucose is present to sustain its high proliferation in vitro. Furthermore, it provided an insight into the parasite biology in the vector environment that supply glucose and the digestion of blood generates free heme that can lead to the growth of T. cruzi epimastigotes.

DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data

The identification of molecular structure is essential for understanding chemical diversity and for developing drug leads from small molecules. Nevertheless, the structure elucidation of small molecules by Nuclear Magnetic Resonance (NMR) experiments is often a long and non-trivial process that relies on years of training. To achieve this process efficiently, several spectral databases have been established to retrieve reference NMR spectra. However, the number of reference NMR spectra available is limited and has mostly facilitated annotation of commercially available derivatives. Here, we introduce DeepSAT, a neural network-based structure annotation and scaffold prediction system that directly extracts the chemical features associated with molecular structures from their NMR spectra. Using only the 1H-13C HSQC spectrum, DeepSAT identifies related known compounds and thus efficiently assists in the identification of molecular structures. DeepSAT is expected to accelerate chemical and biomedical research by accelerating the identification of molecular structures.

Integrated NMR and MS Analysis of the Plasma Metabolome Reveals Major Changes in One-Carbon, Lipid, and Amino Acid Metabolism in Severe and Fatal Cases of COVID-19

Brazil has the second-highest COVID-19 death rate worldwide, and Rio de Janeiro is among the states with the highest rate in the country. Although vaccine coverage has been achieved, it is anticipated that COVID-19 will transition into an endemic disease. It is concerning that the molecular mechanisms underlying clinical evolution from mild to severe disease, as well as the mechanisms leading to long COVID-19, are not yet fully understood. NMR and MS-based metabolomics were used to identify metabolites associated with COVID-19 pathophysiology and disease outcome. Severe COVID-19 cases (n = 35) were enrolled in two reference centers in Rio de Janeiro within 72 h of ICU admission, alongside 12 non-infected control subjects. COVID-19 patients were grouped into survivors (n = 18) and non-survivors (n = 17). Choline-related metabolites, serine, glycine, and betaine, were reduced in severe COVID-19, indicating dysregulation in methyl donors. Non-survivors had higher levels of creatine/creatinine, 4-hydroxyproline, gluconic acid, and N-acetylserine, indicating liver and kidney dysfunction. Several changes were greater in women; thus, patients' sex should be considered in pandemic surveillance to achieve better disease stratification and improve outcomes. These metabolic alterations may be useful to monitor organ (dys) function and to understand the pathophysiology of acute and possibly post-acute COVID-19 syndromes.

NMR as a tool for compound identification in mixtures

INTRODUCTION

Natural products and metabolomics are intrinsically linked through efforts to analyze complex mixtures for compound annotation. Although most studies that aim for compound identification in mixtures use MS as the main analysis technique, NMR has complementary advances that are worth exploring for enhanced structural confidence.

OBJECTIVE

This review aimed to showcase a portfolio of the main tools available for compound identification using NMR.

MATERIALS AND METHODS

COLMAR, SMART-NMR, MADByTE, and NMRfilter are presented using examples collected from real samples from the perspective of a natural product chemist. Data are also made available through Zenodo so that readers can test each case presented here.

CONCLUSION

The acquisition of ¹ H NMR, HSQC, TOCSY, HSQC-TOCSY, and HMBC data for all samples and fractions from a natural products study is strongly suggested. The same is valid for MS analysis to create a bridged analysis between both techniques in a complementary manner. The use of NOAH supersequences has also been suggested and demonstrated to save NMR time.

Advances in Microbial NMR Metabolomics

Confidently, nuclear magnetic resonance (NMR) is the most informative technique in analytical chemistry and its use as an analytical platform in metabolomics is well proven. This chapter aims to present NMR as a viable tool for microbial metabolomics discussing its fundamental aspects and applications in metabolomics using some chosen examples.

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 &gt;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.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics

Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.

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.

Metabolic Hepatic Disorders Caused by Ciguatoxins in Goldfish (Carassius auratus)

Ciguatera poisoning (CP) is a foodborne disease known for centuries; however, little research has been conducted on the effects of ciguatoxins (CTXs) on fish metabolism. The main objective of this study was to assess different hepatic compounds observed in goldfish (Carassius auratus) fed C-CTX1 using nuclear magnetic resonance (NMR)-based metabolomics. Thirteen goldfish were treated with C-CTX1-enriched flesh and sampled on days 1, 8, 15, 29, 36, and 43. On day 43, two individuals, referred to as 'Detox', were isolated until days 102 and 121 to evaluate the possible recovery after returning to a commercial feed. At each sampling, hepatic tissue was weighed to calculate the hepatosomatic index (HSI) and analyzed for the metabolomics study; animals fed toxic flesh showed a higher HSI, even greater in the 'Detox' individuals. Furthermore, altered concentrations of alanine, lactate, taurine, glucose, and glycogen were observed in animals with the toxic diet. These disturbances could be related to an increase in ammonium ion (NH₄⁺) production. An increase in ammonia (NH₃) concentration in water was observed in the aquarium where the fish ingested toxic meat compared to the non-toxic aquarium. All these changes may be rationalized by the relationship between CTXs and the glucose-alanine cycle.

Leveraging the HMBC to Facilitate Metabolite Identification

The accuracy and ease of metabolite assignments from a complex mixture are expected to be facilitated by employing a multispectral approach. The two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) and 2D 1H-1H-total correlation spectroscopy (TOCSY) are the experiments commonly used for metabolite assignments. The 2D 1H-13C HSQC-TOCSY and 2D 1H-13C heteronuclear multiple-bond correlation (HMBC) are routinely used by natural products chemists but have seen minimal usage in metabolomics despite the unique information, the nearly complete 1H-1H and 1H-13C and spin systems provided by these experiments that may improve the accuracy and reliability of metabolite assignments. The use of a 13C-labeled feedstock such as glucose is a routine practice in metabolomics to improve sensitivity and to emphasize the detection of specific metabolites but causes severe artifacts and an increase in spectral complexity in the HMBC experiment. To address this issue, the standard HMBC pulse sequence was modified to include carbon decoupling. Nonuniform sampling was also employed for rapid data collection. A dataset of reference 2D 1H-13C HMBC spectra was collected for 94 common metabolites. 13C-13C spin connectivity was then obtained by generating a covariance pseudo-spectrum from the carbon-decoupled HMBC and the 1H-13C HSQC-TOCSY spectra. The resulting 13C-13C pseudo-spectrum provides a connectivity map of the entire carbon backbone that uniquely describes each metabolite and would enable automated metabolite identification.

SMART-Miner: A convolutional neural network-based metabolite identification from 1 H-13 C HSQC spectra

The identification of metabolites from complex biofluids and extracts of tissues is an essential process for understanding metabolic profiles. Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomics studies for identification and quantification of metabolites. However, the accurate identification of individual metabolites is still a challenging process with higher peak intensity or similar chemical shifts from different metabolites. In this study, we applied a convolutional neural network (CNN) to ¹ H-¹³ C HSQC NMR spectra to achieve accurate peak identification in complex mixtures. The results reveal that the neural network was successfully trained on metabolite identification from these 2D NMR spectra and achieved very good performance compared with other NMR-based metabolomic tools.

NMR and Metabolomics-A Roadmap for the Future

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

Uncovering in vivo biochemical patterns from time-series metabolic dynamics

System biology relies on holistic biomolecule measurements, and untangling biochemical networks requires time-series metabolomics profiling. With current metabolomic approaches, time-series measurements can be taken for hundreds of metabolic features, which decode underlying metabolic regulation. Such a metabolomic dataset is untargeted with most features unannotated and inaccessible to statistical analysis and computational modeling. The high dimensionality of the metabolic space also causes mechanistic modeling to be rather cumbersome computationally. We implemented a faster exploratory workflow to visualize and extract chemical and biochemical dependencies. Time-series metabolic features (about 300 for each dataset) were extracted by Ridge Tracking-based Extract (RTExtract) on measurements from continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) in Neurospora crassa under different conditions. The metabolic profiles were then smoothed and projected into lower dimensions, enabling a comparison of metabolic trends in the cultures. Next, we expanded incomplete metabolite annotation using a correlation network. Lastly, we uncovered meaningful metabolic clusters by estimating dependencies between smoothed metabolic profiles. We thus sidestepped the processes of time-consuming mechanistic modeling, difficult global optimization, and labor-intensive annotation. Multiple clusters guided insights into central energy metabolism and membrane synthesis. Dense connections with glucose 1-phosphate indicated its central position in metabolism in N. crassa. Our approach was benchmarked on simulated random network dynamics and provides a novel exploratory approach to analyzing high-dimensional metabolic dynamics.

Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)

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Automatic assignment of metabolites of 2D-TOCSY NMR spectra.

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Semi-supervised learning for metabolic profiling.

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Deconvolution and metabolic profiling of 2D NMR spectra using Machine Learning.

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Accurate Automatic multicomponent assignment of 2D NMR spectrum.

Metabolomics is an expanding field of medical diagnostics since many diseases cause metabolic reprogramming alteration. Additionally, the metabolic point of view offers an insight into the molecular mechanisms of diseases. Due to the complexity of metabolic assignment dependent on the 1D NMR spectral analysis, 2D NMR techniques are preferred because of spectral resolution issues. Thus, in this work, we introduce an automated metabolite identification and assignment from ¹H-¹H TOCSY (total correlation spectroscopy) using real breast cancer tissue. The new approach is based on customized and extended semi-supervised classifiers: KNFST, SVM, third (PC3) and fourth (PC4) degree polynomial. In our approach, metabolic assignment is based only on the vertical and horizontal frequencies of the metabolites in the ¹H–¹H TOCSY. KNFST and SVM show high performance (high accuracy and low mislabeling rate) in relatively low size of initially labeled training data. PC3 and PC4 classifiers showed lower accuracy and high mislabeling rates, and both classifiers fail to provide an acceptable accuracy at extremely low size (≤9% of the entire dataset) of initial training data. Additionally, semi-supervised classifiers were implemented to obtain a fully automatic procedure for signal assignment and deconvolution of TOCSY, which is a big step forward in NMR metabolic profiling. A set of 27 metabolites were deduced from the TOCSY, and their assignments agreed with the metabolites deduced from a 1D NMR spectrum of the same sample analyzed by conventional human-based methodology.

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.

Recent Developments Toward Integrated Metabolomics Technologies (UHPLC-MS-SPE-NMR and MicroED) for Higher-Throughput Confident Metabolite Identifications

Metabolomics has emerged as a powerful discipline to study complex biological systems from a small molecule perspective. The success of metabolomics hinges upon reliable annotations of spectral features obtained from MS and/or NMR. In spite of tremendous progress with regards to analytical instrumentation and computational tools, < 20% of spectral features are confidently identified in most untargeted metabolomics experiments. This article explores the integration of multiple analytical instruments such as UHPLC-MS/MS-SPE-NMR and the cryo-EM method MicroED to achieve large-scale and confident metabolite identifications in a higher-throughput manner. UHPLC-MS/MS-SPE allows for the simultaneous automated purification of metabolites followed by offline structure elucidation and structure validation by NMR and MicroED. Large-scale study of complex metabolomes such as that of the model plant legume Medicago truncatula can be achieved using an integrated UHPLC-MS/MS-SPE-NMR metabolomics platform. Additionally, recent developments in MicroED to study structures of small organic molecules have enabled faster, easier and precise structure determinations of metabolites. A MicroED small molecule structure elucidation workflow (e.g., crystal screening, sample preparation, data collection and data processing/structure determination) has been described. Ongoing MicroED methods development and its future scope related to structure elucidation of specialized metabolites and metabolomics are highlighted. The incorporation of MicroED with a UHPLC-MS/MS-SPE-NMR instrumental ensemble offers the potential to accelerate and achieve higher rates of metabolite identification.

Development of an NMR-Based Platform for the Direct Structural Annotation of Complex Natural Products Mixtures

The development of new "omics" platforms is having a significant impact on the landscape of natural products discovery. However, despite the advantages that such platforms bring to the field, there remains no straightforward method for characterizing the chemical landscape of natural products libraries using two-dimensional nuclear magnetic resonance (2D-NMR) experiments. NMR analysis provides a powerful complement to mass spectrometric approaches, given the universal coverage of NMR experiments. However, the high degree of signal overlap, particularly in one-dimensional NMR spectra, has limited applications of this approach. To address this issue, we have developed a new data analysis platform for complex mixture analysis, termed MADByTE (Metabolomics and Dereplication by Two-Dimensional Experiments). This platform employs a combination of TOCSY and HSQC spectra to identify spin system features within complex mixtures and then matches spin system features between samples to create a chemical similarity network for a given sample set. In this report we describe the design and construction of the MADByTE platform and demonstrate the application of chemical similarity networks for both the dereplication of known compound scaffolds and the prioritization of bioactive metabolites from a bacterial prefractionated extract library.

Applying NMR compound identification using NMRfilter to match predicted to experimental data

INTRODUCTION

Metabolomics is the approach of choice to guide the understanding of biological systems and its molecular intricacies, but compound identification is yet a bottleneck to be overcome.

OBJECTIVE

To assay the use of NMRfilter for confidence compound identification based on chemical shift predictions for different datasets.

RESULTS

We found comparable results using the lead tool COLMAR and NMRfilter. Then, we successfully assayed the use of HMBC to add confidence to the identified compounds.

CONCLUSIONS

NMRfilter is currently under development to become a stand-alone interactive software for high-confidence NMR compound identification and this communication gathers part of its application capabilities.

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.

Metabolite Structure Assignment Using In Silico NMR Techniques

A major challenge for metabolomic analysis is to obtain an unambiguous identification of the metabolites detected in a sample. Among metabolomics techniques, NMR spectroscopy is a sophisticated, powerful, and generally applicable spectroscopic tool that can be used to ascertain the correct structure of newly isolated biogenic molecules. However, accurate structure prediction using computational NMR techniques depends on how much of the relevant conformational space of a particular compound is considered. It is intrinsically challenging to calculate NMR chemical shifts using high-level DFT when the conformational space of a metabolite is extensive. In this work, we developed NMR chemical shift calculation protocols using a machine learning model in conjunction with standard DFT methods. The pipeline encompasses the following steps: (1) conformation generation using a force field (FF)-based method, (2) filtering the FF generated conformations using the ASE-ANI machine learning model, (3) clustering of the optimized conformations based on structural similarity to identify chemically unique conformations, (4) DFT structural optimization of the unique conformations, and (5) DFT NMR chemical shift calculation. This protocol can calculate the NMR chemical shifts of a set of molecules using any available combination of DFT theory, solvent model, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Our protocol reduces the overall computational time by 2 orders of magnitude over methods that optimize the conformations using fully ab initio methods, while still producing good agreement with experimental observations. The complete protocol is designed in such a manner that makes the computation of chemical shifts tractable for a large number of conformationally flexible metabolites.

Phosphorus NMR and Its Application to Metabolomics

Stable isotopes are routinely employed by NMR metabolomics to highlight specific metabolic processes and to monitor pathway flux. 13C-carbon and 15N-nitrogen labeled nutrients are convenient sources of isotope tracers and are commonly added as supplements to a variety of biological systems ranging from cell cultures to animal models. Unlike 13C and 15N, 31P-phosphorus is a naturally abundant and NMR active isotope that does not require an external supplemental source. To date, 31P NMR has seen limited usage in metabolomics because of a lack of reference spectra, difficulties in sample preparation, and an absence of two-dimensional (2D) NMR experiments, but 31P NMR has the potential of expanding the coverage of the metabolome by detecting phosphorus-containing metabolites. Phosphorylated metabolites regulate key cellular processes, serve as a surrogate for intracellular pH conditions, and provide a measure of a cell's metabolic energy and redox state, among other processes. Thus, incorporating 31P NMR into a metabolomics investigation will enable the detection of these key cellular processes. To facilitate the application of 31P NMR in metabolomics, we present a unified protocol that allows for the simultaneous and efficient detection of 1H-, 13C-, 15N-, and 31P-labeled metabolites. The protocol includes the application of a 2D 1H-31P HSQC-TOCSY experiment to detect 31P-labeled metabolites from heterogeneous biological mixtures, methods for sample preparation to detect 1H-, 13C-, 15N-, and 31P-labeled metabolites from a single NMR sample, and a data set of one-dimensional (1D) 31P NMR and 2D 1H-31P HSQC-TOCSY spectra of 38 common phosphorus-containing metabolites to assist in metabolite assignments.

Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D 1H NMR Database Search

NMR-based metabolomics requires proper identification of metabolites to draw conclusions from the system under study. Normally, multivariate data analysis is performed using 1D ¹H NMR spectra, and identification of peaks (and then compounds) relevant to the classification is accomplished using database queries as a first step. 1D ¹H NMR spectra of complex mixtures often suffer from peak overlap. To overcome this issue, several studies employed the projections of the (tilted and symmetrized) 2D ¹H J-resolved (JRES) spectra, p-JRES, which are similar to 1D ¹H decoupled spectra. Nonetheless, there are no public databases available that allow searching for chemical shift spectral data for multiplets. We present the Chemical Shift Multiplet Database (CSMDB), built utilizing JRES spectra obtained from the Birmingham Metabolite Library. The CSMDB provides scoring accounting for both matched and unmatched peaks from a query list and the database hits. This input list is generated from a projection of a 2D statistical correlation analysis on the JRES spectra, p-(JRES-STOCSY), being able to compare the multiplets for the matched peaks, in essence, the f1 traces from the JRES-STOCSY spectrum and from the database hit. The inspection of the unmatched peaks for the database hit allows the retrieval of peaks in the query list that have a decreased correlation coefficient due to low intensities. The CSMDB is coupled to "ConQuer ABC", which permits the assessment of biological correlation by means of consecutive queries with the unmatched peaks in the first and subsequent queries.

Metabolic Regulation of Glycolysis and AMP Activated Protein Kinase Pathways during Black Raspberry-Mediated Oral Cancer Chemoprevention

Oral cancer is a public health problem with an incidence of almost 50,000 and a mortality of 10,000 each year in the USA alone. Black raspberries (BRBs) have been shown to inhibit oral carcinogenesis in several preclinical models, but our understanding of how BRB phytochemicals affect the metabolic pathways during oral carcinogenesis remains incomplete. We used a well-established rat oral cancer model to determine potential metabolic pathways impacted by BRBs during oral carcinogenesis. F344 rats were exposed to the oral carcinogen 4-nitroquinoline-1-oxide in drinking water for 14 weeks, then regular drinking water for six weeks. Carcinogen exposed rats were fed a 5% or 10% BRB supplemented diet or control diet for six weeks after carcinogen exposure. RNA-Seq transcriptome analysis on rat tongue, and mass spectrometry and NMR metabolomics analysis on rat urine were performed. We tentatively identified 57 differentially or uniquely expressed metabolites and over 662 modulated genes in rats being fed with BRB. Glycolysis and AMPK pathways were modulated during BRB-mediated oral cancer chemoprevention. Glycolytic enzymes Aldoa, Hk2, Tpi1, Pgam2, Pfkl, and Pkm2 as well as the PKA-AMPK pathway genes Prkaa2, Pde4a, Pde10a, Ywhag, and Crebbp were downregulated by BRBs during oral cancer chemoprevention. Furthermore, the glycolysis metabolite glucose-6-phosphate decreased in BRB-administered rats. Our data reveal the novel metabolic pathways modulated by BRB phytochemicals that can be targeted during the chemoprevention of oral cancer.

"-Omics" workflow for paleolimnological and geological archives: A review

"-Omics" is a powerful screening method with applications in molecular biology, toxicology, wildlife biology, natural product discovery, and many other fields. Genomics, proteomics, metabolomics, and lipidomics are common examples included under the "-omics" umbrella. This screening method uses combinations of untargeted, semi-targeted, and targeted analyses paired with data mining to facilitate researchers' understanding of the genome, proteins, and small organic molecules in biological systems. Recently, however, the use of "-omics" has expanded into the fields of geology, specifically petrology, and paleolimnology. Specifically, untargeted analyses stand to transform these fields as petroleomics, and sediment-"omics" become more prevalent. "-Omics" facilitates the visualization of small molecule profiles from environmental matrices (i.e. oil and sediment). Small molecule profiles can provide improved understanding of small molecules distributions throughout the environment, and how those compositions can change depending on conditions (i.e. climate change, weathering, etc.). "-Omics" also facilities discovery of next-generation biomarkers that can be used for oil source identification and as proxies for reconstructing past environmental changes. Untargeted analyses paired with data mining and multivariate statistical analyses represents a powerful suite of tools for hypothesis generation, and new method development for environmental reconstructions. Here we present an introduction to "-omics" methodology, technical terms, and examples of applications to paleolimnology and petrology. The purpose of this review is to highlight the important considerations at each step in the "-omics" workflow to produce high quality and statistically powerful data for petrological and paleolimnological applications.

Extractive Ratio Analysis NMR Spectroscopy for Metabolite Identification in Complex Biological Mixtures

The complexity of biological mixtures continues to challenge efforts aimed at unknown metabolite identification in the metabolomics field. To address this challenge, we provide a new method to identify related peaks from individual metabolites in complex NMR spectra. Extractive ratio analysis NMR spectroscopy (E-RANSY) builds on our previously described ratio analysis method [ Wei et al. Anal. Chem. 2011 , 83 , 7616 - 7623 ] and exploits the simplified NMR spectra provided by the extraction of metabolites under varied pH conditions. Under such conditions, metabolites from the same biological specimen are extracted differentially, and the resulting NMR spectra exhibit characteristics favorable for unraveling unknown metabolite peaks using ratio analysis. We demonstrate the utility of the E-RANSY method by extracting carboxylic acid containing metabolites from human urine, one of the highly complex biological mixtures encountered in the metabolomics field. E-RANSY performs better than STOCSY and the original RANSY method and offers new avenues to identify unknown metabolites in complex biological mixtures.

13C Labeling of Nematode Worms to Improve Metabolome Coverage by Heteronuclear Nuclear Magnetic Resonance Experiments

Nuclear magnetic resonance (NMR) spectroscopy is widely used as a metabolomics tool, and 1D spectroscopy is overwhelmingly the commonest approach. The use of 2D spectroscopy could offer significant advantages in terms of increased spectral dispersion of peaks, but has a number of disadvantages—in particular, heteronuclear 2D spectroscopy is often much less sensitive than 1D NMR. One factor contributing to this low sensitivity in ¹³C/¹H heteronuclear NMR is the low natural abundance of the ¹³C stable isotope; as a consequence, where it is possible to label biological material with ¹³C, there is a potential enhancement of sensitivity of up to around 90fold. However, there are some problems that can reduce the advantages otherwise gained—in particular, the fine structure arising from ¹³C/¹³C coupling, which is essentially non-existent at natural abundance, can reduce the possible sensitivity gain and increase the chances of peak overlap. Here, we examined the use of two different heteronuclear single quantum coherence (HSQC) pulse sequences for the analysis of fully ¹³C-labeled tissue extracts from Caenorhabditis elegans nematodes. The constant time ct-HSQC had improved peak shape, and consequent better peak detection of metabolites from a labeled extract; matching this against reference spectra from the HMDB gave a match to about 300 records (although fewer actual metabolites, as some of these represent false positive matches). This approach gives a rapid and automated initial metabolome assignment, forming an ideal basis for further manual curation.

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.

Carbohydrate Background Removal in Metabolomics Samples

NMR-based metabolomics is a powerful tool to comprehensively monitor chemical processes in biological systems. Key to its success is the accurate and complete metabolite identification and quantification. Due to the inherent complexity of most metabolic mixtures, NMR peak overlap can make data analysis of 1D or even 2D NMR spectra challenging, especially for the ¹H spectral region from 3.2-4.5 ppm that is dominated by carbohydrates and their derivatives. To address this problem, we present an effective method for carbohydrate signal removal in complex metabolomics samples by oxidation via the addition of sodium periodate (NaIO₄). In an optional step, reaction products can be removed with hydrazide beads. The treated samples show substantially simplified 1D and 2D NMR spectra with their carbohydrate peaks removed, whereas noncarbohydrate peaks remain mostly unaffected. This allows the unrestricted detection of those metabolites that are otherwise obscured by carbohydrate signals. The method was first tested for metabolite model mixtures and then applied to urine and serum samples. It revealed a significant number of noncarbohydrates that were made unambiguously observable and identifiable by this method. The proposed protocol is simple and it is suitable for high-throughput sample treatment for the comprehensive metabolite identification in a broad range of samples.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

NMR and MS-based Stable Isotope-Resolved Metabolomics and Applications in Cancer Metabolism

There is considerable interest in defining metabolic reprogramming in human diseases, which is recognized as a hallmark of human cancer. Although radiotracers have a long history in specific metabolic studies, stable isotope-enriched precursors coupled with modern high resolution mass spectrometry and NMR spectroscopy have enabled systematic mapping of metabolic networks and fluxes in cells, tissues and living organisms including humans. These analytical platforms are high in information content, are complementary and cross-validating in terms of compound identification, quantification, and isotope labeling pattern analysis of a large number of metabolites simultaneously. Furthermore, new developments in chemoselective derivatization and in vivo spectroscopy enable tracking of labile/low abundance metabolites and metabolic kinetics in real-time. Here we review developments in Stable Isotope Resolved Metabolomics (SIRM) and recent applications in cancer metabolism using a wide variety of stable isotope tracers that probe both broad and specific aspects of cancer metabolism required for proliferation and survival.

Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation

Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by gas chromatography-MS, 20 by NMR, and 22 by both techniques). Out of these, 47 metabolites of interest were identified: 14 metabolites were uniquely identified by NMR, and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism, leading to fatty-acid and complex-lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques: the improved detection and annotation of metabolites.

NMR-based urine metabolic profiling and immunohistochemistry analysis of nephron changes in a mouse model of hypoxia-induced acute kidney injury

Acute kidney injury can be caused by multiple factors, including sepsis, respiratory failure, heart failure, trauma, or nephrotoxic medications, among others. Here, a mouse model was used to investigate potential urinary metabolic biomarkers of hypoxia-induced AKI. Urine metabolic profiles of 48 Swiss Webster mice were assessed using nuclear magnetic resonance spectroscopy (NMR) for 7 days following 72 h exposure to a hypoxic 6.5% oxygen environment. Histological analyses indicated a lack of gross nephron structural changes in the aftermath of hypoxia. Immunohistochemical (IHC) analyses, however, indicated elevated expression of protein injury biomarkers in distal and proximal tubules but not glomeruli. Kidney injury molecule-1 levels peaked in distal tubules at 72 h and were still increasing in proximal tubules at 7 days posthypoxia, whereas cystatin C levels were elevated at 24 h but decreased thereafter, and were elevated and still increasing in proximal tubules at 7 days posthypoxia. Neutrophil gelatinase-associated lipocalin levels were modestly elevated from 24 h to 7 days posthypoxia. NMR-based metabolic profiling revealed that urine metabolites involved in energy metabolism and associated biosynthetic pathways were initially decreased at 24 h posthypoxia, consistent with metabolic suppression as a mechanism for cell survival, but were significantly elevated at 48 and 72 h posthypoxia, indicating a burst in organism metabolism associated with reactivation of cellular energetics during recovery after cessation of hypoxia and return to a normoxic environment. The IHC results indicated that kidney injury persists long after plasma and urine biomarkers of hypoxia return to normal values.

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.

Metabolic Biomarkers of Ageing in C57BL/6J Wild-Type and Flavin-Containing Monooxygenase 5 (FMO5)-Knockout Mice

It was recently demonstrated in mice that knockout of the flavin-containing monooxygenase 5 gene, Fmo5, slows metabolic ageing via pleiotropic effects. We have now used an NMR-based metabonomics approach to study the effects of ageing directly on the metabolic profiles of urine and plasma from male, wild-type C57BL/6J and Fmo5^-/- (FMO5 KO) mice back-crossed onto the C57BL/6J background. The aim of this study was to identify metabolic signatures that are associated with ageing in both these mouse lines and to characterize the age-related differences in the metabolite profiles between the FMO5 KO mice and their wild-type counterparts at equivalent time points. We identified a range of age-related biomarkers in both urine and plasma. Some metabolites, including urinary 6-hydroxy-6-methylheptan-3-one (6H6MH3O), a mouse sex pheromone, showed similar patterns of changes with age, regardless of genetic background. Others, however, were altered only in the FMO5 KO, or only in the wild-type mice, indicating the impact of genetic modifications on mouse ageing. Elevated concentrations of urinary taurine represent a distinctive, ageing-related change observed only in wild-type mice.

Caloric Restriction Engages Hepatic RNA Processing Mechanisms in Rhesus Monkeys

Caloric restriction (CR) extends lifespan and delays the onset of age-related disorders in diverse species. Metabolic regulatory pathways have been implicated in the mechanisms of CR, but the molecular details have not been elucidated. Here, we show that CR engages RNA processing of genes associated with a highly integrated reprogramming of hepatic metabolism. We conducted molecular profiling of liver biopsies collected from adult male rhesus monkeys (Macaca mulatta) at baseline and after 2 years on control or CR (30% restricted) diet. Quantitation of over 20,000 molecules from the hepatic transcriptome, proteome, and metabolome indicated that metabolism and RNA processing are major features of the response to CR. Predictive models identified lipid, branched-chain amino acid, and short-chain carbon metabolic pathways, with alternate transcript use for over half of the genes in the CR network. We conclude that RNA-based mechanisms are central to the CR response and integral in metabolic reprogramming.

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS²), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS² approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.

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.

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

Metabolic profiling by NMR spectroscopy or hyphenated mass spectrometry, known as metabonomics or metabolomics, is an important tool for systems-based approaches in biology and medicine. The experiments are typically done in a diagnostic fashion where changes in metabolite profiles are interpreted as a consequence of an intervention or event; be that a change in diet, the administration of a drug, physical exertion or the onset of a disease. By contrast, pharmacometabonomics takes a prognostic approach to metabolic profiling, in order to predict the effects of drug dosing before it occurs. Differences in pre-dose metabolite profiles between groups of subjects are used to predict post-dose differences in response to drug administration. Thus the paradigm is inverted and pharmacometabonomics is the metabolic equivalent of pharmacogenomics. Although the field is still in its infancy, it is expected that pharmacometabonomics, alongside pharmacogenomics, will assist with the delivery of personalised or precision medicine to patients, which is a critical goal of 21st century healthcare.

NMR spectroscopy and electron microscopy identification of metabolic and ultrastructural changes to the kidney following ischemia-reperfusion injury

Cellular, molecular, and ultrastructural nephron changes associated with ischemia-reperfusion injury-induced acute kidney injury (IRI-AKI) are not completely understood. Here, a multidisciplinary study was used to identify nephron changes in a mouse model of IRI-AKI. Histological analyses indicated distended Bowman's glomerular spaces and proximal and distal tubules. Increased filtrate volume in nephrons was caused by reduced water reabsorption by severely damaged proximal tubule brush borders and blocked flow of filtrate into collecting tubules by mucoprotein casts in distal tubules. Immunohistochemistry revealed protein AKI biomarkers in proximal tubules and glomeruli but not in distal tubules. Nuclear magnetic resonance spectroscopy revealed several metabolites that increased such as valine, alanine, and lactate. Other metabolites such as trigonelline, succinate, 2-oxoisocaproate, and 1- methyl-nicotinamide decreased or were absent in urine following IRI due to altered kidney function or metabolism. Urinary glucose increased due to reduced reabsorption by damaged proximal tubule brush borders. Scanning electron microscopy revealed flattening of podocytes and pedicals surrounding glomerular capillaries, and transmission electron microscopy (TEM) revealed effacement of podocyte pedicals, both consistent with increased hydrostatic pressure in nephrons following IRI-AKI. TEM revealed shortened proximal tubule microvilli in IRI kidneys with diminished lamina propia. TEM showed dramatic loss of mitochondria in distal tubule epithelia of IRI kidneys and emergence of multivesicular bodies of endosomes indicating ongoing cellular death. Collectively, the data define ultrastructural changes to nephrons and altered kidney metabolism associated with IRI-AKI.

Metabolomic Evaluation of the Consequences of Plasma Cystathionine Elevation in Adults with Stable Angina Pectoris

Background: An elevated circulating cystathionine concentration, which arises in part from insufficiencies of vitamin B-6, B-12, or folate, has been shown to be associated with cardiovascular disease (CVD) risk. Hydrogen sulfide (H₂S) is a gasotransmitter involved in vasodilation, neuromodulation, and inflammation. Most endogenously produced H₂S is formed by pyridoxal phosphate (PLP)-dependent enzymes by noncanonical reactions of the transsulfuration pathway that yield H₂S concurrently form lanthionine and homolanthionine. Thus, plasma lanthionine and homolanthionine concentrations can provide relative information about H₂S production in vivo.Objective: To determine the metabolic consequences of an elevated plasma cystathionine concentration in adults with stable angina pectoris (SAP), we conducted both targeted and untargeted metabolomic analyses.Methods: We conducted NMR and LC-mass spectrometry (MS) metabolomic analyses on a subset of 80 plasma samples from the Western Norway Coronary Angiography Cohort and selected, based on plasma cystathionine concentrations, a group with high cystathionine concentrations [1.32 ± 0.60 μmol/L (mean ± SD); n = 40] and a group with low cystathionine concentrations [0.137 ± 0.011 μmol/L (mean ± SD); n = 40]. Targeted and untargeted metabolomic analyses were performed and assessed with the use of Student's t tests corrected for multiple testing. Overall differences between the cystathionine groups were assessed by untargeted NMR and LC-MS metabolomic methods and evaluated by partial least squares discriminant analysis (PLS-DA) with significant discriminating metabolites identified with 99% confidence.Results: Subjects with high cystathionine concentrations had 75% higher plasma lanthionine concentrations (0.12 ± 0.044 μmol/L) than subjects with low cystathionine concentrations [0.032 ± 0.013 μmol/L (P < 0.001)]. Although plasma homolanthionine concentrations were notably higher than lanthionine concentrations, they were not different between the groups (P = 0.47). PLS-DA results showed that a high plasma cystathionine concentration in SAP was associated with higher glucose, branched-chain amino acids, and phenylalanine concentrations, lower kidney function, and lower glutathione and plasma PLP concentrations due to greater catabolism. The high-cystathionine group had a greater proportion of subjects in the postprandial state.Conclusion: These data suggest that metabolic perturbations consistent with higher CVD risk exist in SAP patients with elevated plasma cystathionine concentrations.

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.

Differential Attenuation of NMR Signals by Complementary Ion-Exchange Resin Beads for De Novo Analysis of Complex Metabolomics Mixtures

A primary goal of metabolomics is the characterization of a potentially very large number of metabolites that are part of complex mixtures. Application to biofluids and tissue samples offers insights into biochemical metabolic pathways and their role in health and disease. 1D ¹ H and 2D ¹³ C-¹ H HSQC NMR spectra are most commonly used for this purpose. They yield quantitative information about each proton of the mixture, but do not tell which protons belong to the same molecule. Interpretation requires the use of NMR spectral databases, which naturally limits these investigations to known metabolites. Here, a new method is presented that uses complementary ion exchange resin beads to differentially attenuate 2D NMR cross-peaks that belong to different metabolites. Based on their characteristic attenuation patterns, cross-peaks could be clustered and assigned to individual molecules, including unknown metabolites with multiple spin systems, as demonstrated for a metabolite model mixture and E. coli cell lysate.

Improved Quantum Chemical NMR Chemical Shift Prediction of Metabolites in Aqueous Solution toward the Validation of Unknowns

A quantum-chemistry based protocol, termed MOSS-DFT, is presented for the prediction of ¹³C and ¹H NMR chemical shifts of a wide range of organic molecules in aqueous solution, including metabolites. Molecular motif-specific linear scaling parameters are reported for five different density functional theory (DFT) methods (B97-2/pcS-1, B97-2/pcS-2, B97-2/pcS-3, B3LYP/pcS-2, and BLYP/pcS-2), which were applied to a large set of 176 metabolite molecules. The chemical shift root-mean-square deviations (RMSD) for the best method, B97-2/pcS-3, are 1.93 and 0.154 ppm for ¹³C and ¹H chemical shifts, respectively. Excellent results have been obtained for chemical shifts of methyl and aromatic ¹³C and ¹H that are not directly bonded to a heteroatom (O, N, S, or P) with RMSD values of 1.15/0.079 and 1.31/0.118 ppm, respectively. This study not only demonstrates how NMR chemical shift in aqueous environment can be improved over the commonly used global linear scaling approach, but also allows for motif-specific error estimates, which are useful for an improved chemical shift-based verification of metabolite candidates of metabolomics samples containing unknown components.