Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods

Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning

NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, "solvent-aware" experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced "prosper") has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.

Effect of phenolics on soil microbe distribution, plant growth, and gall formation

Phenolic compounds, abundant secondary metabolites in plants, profoundly influence soil ecosystems, plant growth, and interactions with herbivores. In this study, we explore the intricate relationships between phenolics, soil microbes, and gall formation in Ageratina adenophora (A. adenophora), an invasive plant species in China known for its allelopathic traits. Using metabolomic and microbial profiling, significant differences in soil microbial composition and metabolite profiles were observed between bulk and rhizosphere soil samples. Phenolics influenced bacterial communities, with distinct microbial populations enriched in each soil type. Additionally, phenolics impacted soil metabolic processes, with variations observed in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis between different soil treatments. Analysis of phenolic content in plant and soil samples revealed considerable variations, with higher concentrations observed in certain plant tissues and soil types. Bioactive phenols extracted from plant and soil samples were identified using gas chromatography/mass spectrometry (GC-MS), providing insights into the diverse chemical composition of these compounds. Furthermore, the effects of phenolics on plant growth and gall formation were investigated. Phenols exhibited both stimulatory and inhibitory effects on plant growth, with optimal concentrations promoting emergence but higher concentrations hindering growth. Gall formation was influenced by phenolic concentrations, leading to structural alterations in stem tissue and gall morphology. Histochemical analysis revealed starch and lipid accumulation in gall tissues, indicating metabolic changes induced by phenolics. The presence of phenolics disrupted tissue structures and influenced vascular bundle orientation in gall tissues. Overall, our study highlights the multifaceted roles of phenolic compounds in soil ecosystems, plant development, and gall formation, facilitating the utilization of secondary metabolites in agriculture.

The hidden treasures in endophytic fungi: a comprehensive review on the diversity of fungal bioactive metabolites, usual analytical methodologies, and applications

This review provides a comprehensive overview of the key aspects of the natural metabolite production by endophytic fungi, which has attracted significant attention due to its diverse biological activities and wide range of applications. Synthesized by various fungal species, these metabolites encompass compounds with therapeutic, agricultural, and commercial significance. We delved into strategies and advancements aimed at optimizing fungal metabolite production. Fungal cultivation, especially by Aspergillus, Penicillium, and Fusarium, plays a pivotal role in metabolite biosynthesis, and researchers have explored both submerged and solid-state cultivation processes to harness the full potential of fungal species. Nutrient optimization, pH, and temperature control are critical factors in ensuring high yields of the targeted bioactive metabolites especially for scaling up processes. Analytical methods that includes High-Performance Liquid Chromatography (HPLC), Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS), are indispensable for the identification and quantification of the compounds. Moreover, genetic engineering and metabolic pathway manipulation have emerged as powerful tools to enhance metabolite production and develop novel fungal strains with increased yields. Regulation and control mechanisms at the genetic, epigenetic, and metabolic levels are explored to fine-tune the biosynthesis of fungal metabolites. Ongoing research aims to overcome the complexity of the steps involved to ensure the efficient production and utilization of fungal metabolites.

Present and future of pure shift NMR in metabolomics

NMR is one of the most powerful techniques for the analysis of biological samples in the field of metabolomics. However, the high complexity of fluids, tissues, or other biological materials taken from living organisms is still a challenge for state-of-the-art pulse sequences, thereby limiting the detection, the identification, and the quantification of metabolites. In this context, the resolution enhancement provided by broadband homonuclear decoupling methods, which allows for simplifying 1 H multiplet patterns into singlets, has placed this so-called pure shift technique as a promising approach to perform metabolic profiling with unparalleled level of detail. In recent years, the many advances achieved in the design of pure shift experiments has paved the way to the analysis of a wide range of biological samples with ultra-high resolution. This review leads the reader from the early days of the main pure shift methods that have been successfully developed over the last decades to address complex samples, to the most recent and promising applications of pure shift NMR to the field of NMR-based metabolomics.

Metabolic variation and oxidative stress response of blue mussels (Mytilus sp.) perturbed by norfloxacin exposure

Antibiotics are currently widely applied in agricultural cultivation, animal husbandry, and medical treatment, but the effects and ecological risks of antibiotics need to be further investigated. Norfloxacin is one of the most widely applied fluoroquinolone antibiotics and is commonly detected in aquatic ecosystems. In this study, the activities of catalase (CAT) and glutathione S-transferase (GST) in blue mussels (Mytilus sp.) exposed to norfloxacin (from 25 to 200 mg/L) for 2 d of acute exposure and 7 d of subacute exposure were measured. ¹H nuclear magnetic resonance (¹H-NMR)-based metabolomics was applied to identify the metabolites and to investigate the physiological metabolism of blue mussels (Mytilus sp.) under different concentrations of norfloxacin. The activity of the CAT enzyme was induced in acute exposure, while the activity of GST was inhibited in subacute exposure when the concentration of norfloxacin reached 200 mg/L. Orthogonal partial least squares discriminant analysis (OPLS-DA) revealed that the increased concentrations of norfloxacin might cause greater metabolic differences between the treatment and control groups and cause greater metabolic variation within the same treatment group. The contents of taurine in the 150 mg/L acute exposure group were 5.17 times higher than those in the control group. The pathway analysis indicated that exposure to high concentrations of norfloxacin disturbed different pathways involved in energy metabolism, amino acid metabolism, neuroregulation, and the regulation of osmotic pressure. These results may provide a molecular and metabolic view of the effects of norfloxacin and the regulatory mechanism of blue mussels when exposed to extremely high doses of antibiotics.

Developmental programming: Preconceptional and gestational exposure of sheep to a real-life environmental chemical mixture alters maternal metabolome in a fetal sex-specific manner

BACKGROUND

Everyday, humans are exposed to a mixture of environmental chemicals some of which have endocrine and/or metabolism disrupting actions which may contribute to non-communicable diseases. The adverse health impacts of real-world chemical exposure, characterized by chronic low doses of a mixture of chemicals, are only recently emerging. Biosolids derived from human waste represent the environmental chemical mixtures humans are exposed to in real life. Prior studies in sheep have shown aberrant reproductive and metabolic phenotypes in offspring after maternal biosolids exposure.

OBJECTIVE

To determine if exposure to biosolids perturbs the maternal metabolic milieu of pregnant ewes, in a fetal sex-specific manner.

METHODS

Ewes were grazed on inorganic fertilizer (Control) or biosolids-treated pastures (BTP) from before mating and throughout gestation. Plasma from pregnant ewes (Control n = 15, BTP n = 15) obtained mid-gestation were analyzed by untargeted metabolomics. Metabolites were identified using Agilent MassHunter. Multivariate analyses were done using MetaboAnalyst 5.0 and confirmed using SIMCA.

RESULTS

Univariate and multivariate analysis of 2301 annotated metabolites identified 193 differentially abundant metabolites (DM) between control and BTP sheep. The DM primarily belonged to the super-class of lipids and organic acids. 15-HeTrE, oleamide, methionine, CAR(3:0(OH)) and pyroglutamic acid were the top DM and have been implicated in the regulation of fetal growth and development. Fetal sex further exacerbated differences in metabolite profiles in the BTP group. The organic acids class of metabolites was abundant in animals with male fetuses. Prenol lipid, sphingolipid, glycerolipid, alkaloid, polyketide and benzenoid classes showed fetal sex-specific responses to biosolids.

DISCUSSION

Our study illustrates that exposure to biosolids significantly alters the maternal metabolome in a fetal sex-specific manner. The altered metabolite profile indicates perturbations to fatty acid, arginine, branched chain amino acid and one‑carbon metabolism. These factors are consistent with, and likely contribute to, the adverse phenotypic outcomes reported in the offspring.

MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication

Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.

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.

In Silico Collision Cross Section Calculations to Aid Metabolite Annotation

The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.

Plant Gravitropism: From Mechanistic Insights into Plant Function on Earth to Plants Colonizing Other Worlds

Gravitropism, the growth of roots and shoots toward or away from the direction of gravity, has been studied for centuries. Such studies have not only led to a better understanding of the gravitropic process itself, but also paved new paths leading to deeper mechanistic insights into a wide range of research areas. These include hormone biology, cell signal transduction, regulation of gene expression, plant evolution, and plant interactions with a variety of environmental stimuli. In addition to contributions to basic knowledge about how plants function, there is accumulating evidence that gravitropism confers adaptive advantages to crops, particularly under marginal agricultural soils. Therefore, gravitropism is emerging as a breeding target for enhancing agricultural productivity. Moreover, research on gravitropism has spawned several studies on plant growth in microgravity that have enabled researchers to uncouple the effects of gravity from other tropisms. Although rapid progress on understanding gravitropism witnessed during the past decade continues to be driven by traditional molecular, physiological, and cell biological tools, these tools have been enriched by technological innovations in next-generation omics platforms and microgravity analog facilities. In this chapter, we review the field of gravitropism by highlighting recent landmark studies that have provided unique insights into this classic research topic while also discussing potential contributions to agriculture on Earth and beyond.

Metabolomics of neurological disorders in India

Metabolomics is the comprehensive study of the metabolome and its alterations within biological fluids and tissues. Over the years, applications of metabolomics have been explored in several areas, including personalised medicine in diseases, metabolome-wide association studies (MWAS), pharmacometabolomics and in combination with other branches of omics such as proteomics, transcriptomics and genomics. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the major analytical techniques widely employed in metabolomics. In addition, MS is coupled with chromatography techniques like gas chromatography (GC) and liquid chromatography (LC) to separate metabolites before analysis. These analytical techniques have made possible identification and quantification of large numbers of metabolites, encompassing characterization of diseases and facilitating a systematic and rational therapeutic strategy based on metabolic patterns. In recent years, the metabolomics approach has been used to obtain a deeper insight into the underlying biochemistry of neurodegenerative disorders and the discovery of biomarkers of clinical implications. The current review mainly focuses on an Indian perspective of metabolomics for the identification of metabolites and metabolic alterations serving as potential diagnostic biomarkers for neurological diseases including acute spinal cord injury, amyotrophic lateral sclerosis, tethered cord syndrome, spina bifida, stroke, Parkinson's disease, glioblastoma and neurological disorders with inborn errors of metabolism.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Intraspecific variation in polar and nonpolar metabolite profiles of a threatened Caribbean coral

INTRODUCTION

Research aimed at understanding intraspecific variation among corals could substantially increase understanding of coral biology and improve outcomes of active restoration efforts. Metabolomics is useful for identifying physiological drivers leading to variation among genotypes and has the capacity to improve our selection of candidate corals that express phenotypes beneficial to restoration.

OBJECTIVES

Our study aims to compare metabolomic profiles among known, unique genotypes of the threatened coral Acropora cervicornis. In doing so, we seek information related to the physiological characteristics driving variation among genotypes, which could aid in identifying genets with desirable traits for restoration.

METHODS

We applied proton nuclear magnetic resonance (¹H-NMR) and liquid chromatography-mass spectrometry (LC-MS) to identify and compare metabolomic profiles for seven unique genotypes of A. cervicornis that previously exhibited phenotypic variation in a common garden coral nursery.

RESULTS

Significant variation in polar and nonpolar metabolite profiles was found among A. cervicornis genotypes. Despite difficulties identifying all significant metabolites driving separation among genotypes, our data support previous findings and further suggest metabolomic profiles differ among various genotypes of the threatened species A. cervicornis.

CONCLUSION

The implementation of metabolomic analyses allowed identification of several key metabolites driving separation among genotypes and expanded our understanding of the A. cervicornis metabolome. Although our research is specific to A. cervicornis, these findings have broad relevance for coral biology and active restoration. Furthermore, this study provides specific information on the understudied A. cervicornis metabolome and further confirmation that differences in metabolome structure could drive phenotypic variation among genotypes.

A non-targeted metabolomics study on Xylella fastidiosa infected olive plants grown under controlled conditions

In the last decade, the bacterial pathogen Xylella fastidiosa has devastated olive trees throughout Apulia region (Southern Italy) in the form of the disease called "Olive Quick Decline Syndrome" (OQDS). This study describes changes in the metabolic profile due to the infection by X. fastidiosa subsp. pauca ST53 in artificially inoculated young olive plants of the susceptible variety Cellina di Nardò. The test plants, grown in a thermo-conditioned greenhouse, were also co-inoculated with some xylem-inhabiting fungi known to largely occur in OQDS-affected trees, in order to partially reproduce field conditions in terms of biotic stress. The investigations were performed by combining NMR spectroscopy and MS spectrometry with a non-targeted approach for the analysis of leaf extracts. Statistical analysis revealed that Xylella-infected plants were characterized by higher amounts of malic acid, formic acid, mannitol, and sucrose than in Xylella-non-infected ones, whereas it revealed slightly lower amounts of oleuropein. Attention was paid to mannitol which may play a central role in sustaining the survival of the olive tree against bacterial infection. This study contributes to describe a set of metabolites playing a possible role as markers in the infections by X. fastidiosa in olive.

The utility of nuclear magnetic resonance spectroscopy in assisted reproduction

Infertility affects approximately 15-20% of individuals of reproductive age worldwide. Over the last 40 years, assisted reproductive technology (ART) has helped millions of childless couples. However, ART is limited by a low success rate and risk of multiple gestations. Devising methods for selecting the best gamete or embryo that increases the ART success rate and prevention of multiple gestation has become one of the key goals in ART today. Special emphasis has been placed on the development of non-invasive approaches, which do not require perturbing the embryonic cells, as the current morphology-based embryo selection approach has shortcomings in predicting the implantation potential of embryos. An observed association between embryo metabolism and viability has prompted researchers to develop metabolomics-based biomarkers. Nuclear magnetic resonance (NMR) spectroscopy provides a non-invasive approach for the metabolic profiling of tissues, gametes and embryos, with the key advantage of having a minimal sample preparation procedure. Using NMR spectroscopy, biologically important molecules can be identified and quantified in intact cells, extracts or secretomes. This, in turn, helps to map out the active metabolic pathways in a system. The present review covers the contribution of NMR spectroscopy in assisted reproduction at various stages of the process.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.

Enantioselectivity effects of imazethapyr enantiomers to metabolic responses in mice

Imazethapyr (IMZT) is a typical chiral pesticide with two enantiomers with the R-IMZT having the main herbicidal activity. However, the enantioselectivity of the effects of IMZT enantiomers on human and animals is still unclear. In this study, a nuclear magnetic resonance (NMR)-based metabolomics method and determination of oxidative stress were used to evaluate the enantioselectivity of IMZT enantiomers in mice. The results showed that the R-IMZT caused larger disturbances of endogenous metabolites and the S-IMZT had stronger interferences to oxidation defense system. The significantly perturbed metabolic pathways in mice exposed to the R-enantiomer were the valine, leucine and isoleucine biosynthesis pathway as well as the phenylalanine, tyrosine and tryptophan biosynthesis pathway. However, exposure of mice to the S-enantiomer did not significantly affect the metabolic pathways, but exposure led to an increase of catalase (CAT) activity and an increase in malondialdehyde (MDA) content in the liver. These results indicate that we need to conduct a more comprehensive assessment of the health risks of pesticide monomers in the future. In a word, these results provide more evidence for assessing the differences in health risks of IMZT enantiomers to mammals as well as provide more references for the promotion and use of pesticide monomers in the future.

Chemical Discovery in the Era of Metabolomics

Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true "novel" compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.

Analysis of NMR Metabolomics Data

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

Innovative omics-based approaches for prioritisation and targeted isolation of natural products - new strategies for drug discovery

Covering: 2013 to 2019 The exploration of the chemical diversity of extracts from various biological sources has led to major drug discoveries. Over the past two decades, despite the introduction of advanced methodologies for natural product (NP) research (e.g., dereplication and high content screening), successful accounts of the validation of NPs as lead therapeutic candidates have been limited. In this context, one of the main challenges faced is related to working with crude natural extracts because of their complex composition and the inadequacies of classical bioguided isolation studies given the pace of high-throughput screening campaigns. In line with the development of metabolomics, genomics and chemometrics, significant advances in metabolite profiling have been achieved and have generated high-quality massive genome and metabolome data on natural extracts. The unambiguous identification of each individual NP in an extract using generic methods remains challenging. However, the establishment of structural links among NPs via molecular network analysis and the determination of common features of extract composition have provided invaluable information to the scientific community. In this context, new multi-informational-based profiling approaches integrating taxonomic and/or bioactivity data can hold promise for the discovery and development of new bioactive compounds and return NPs back to an exciting era of development. In this article, we examine recent studies that have the potential to improve the efficiency of NP prioritisation and to accelerate the targeted isolation of key NPs. Perspectives on the field's evolution are discussed.

Cyberecoethnopharmacolomics

ETHNOPHARMACOLOGICAL RELEVANCE

Development of a new term which describes the contemporary, composite, constituent sciences of ethnopharmacology.

AIM OF THE STUDY

To discuss the polysyllabic term cyberecoethnopharmacolomics in the context of the future of ethnopharmacology in global health care.

MATERIALS AND METHODS

Literature background and assessment from the prior literature, diverse databases, and personal discussions.

RESULTS

The profiles and literature background with contemporary and future thoughts regarding the concepts and practices of cyber-, eco-, ethno-, pharmacol-, and -omics, and their impact in ethnopharmacology for the future are presented in the context of integrated health care systems.

CONCLUSIONS

Ethnopharmacology has a major role to play in global health care if the relevant sciences and cutting-edge technologies can coalesce synergistically as a responsive, evidence-based health care practice.

Systems-level analysis of isotopic labeling in untargeted metabolomic data by X13CMS

Identification of previously unreported metabolites (so-called 'unknowns') in untargeted metabolomic data has become an increasingly active area of research. Considerably less attention, however, has been dedicated to identifying unknown metabolic pathways. Yet, for each unknown metabolite structure, there is potentially a yet-to-be-discovered chemical transformation. Elucidating these biochemical connections is essential to advancing our knowledge of cellular metabolism and can be achieved by tracking an isotopically labeled precursor to an unexpected product. In addition to their role in mapping metabolic fates, isotopic labels also provide critical insight into pathway dynamics (i.e., metabolic fluxes) that cannot be obtained from conventional label-free metabolomic analyses. When labeling is compared quantitatively between conditions, for example, isotopic tracers can enable relative pathway activities to be inferred. To discover unexpected chemical transformations or unanticipated differences in metabolic pathway activities, we have developed X13CMS, a platform for analyzing liquid chromatography/mass spectrometry (LC/MS) data at the systems level. After providing cells, animals, or patients with an isotopically enriched metabolite (e.g., 13C, 15N, or 2H), X13CMS identifies compounds that have incorporated the isotopic tracer and reports the extent of labeling for each. The analysis can be performed with a single condition, or isotopic fates can be compared between multiple conditions. The choice of which metabolite to enrich and which isotopic label to use is highly context dependent, but 13C-glucose and 13C-glutamine are often applied because they feed a large number of metabolic pathways. X13CMS is freely available.

A Deeper Investigation of Drug Degradation Mixtures Using a Combination of MS and NMR Data: Application to Indapamide

A global approach that is based on a combination of mass spectrometry (MS) and nuclear magnetic resonance (NMR) data has been developed for a complete and rapid understanding of drug degradation mixtures. We proposed a workflow based on a sample preparation protocol that is compatible to MS and NMR, the selection of the most appropriate experiments for each technique, and the implementation of prediction software and multivariable analysis method for a better interpretation and correlation of MS and NMR spectra. We have demonstrated the efficient quantification of the remaining active pharmaceutical ingredient (API). The unambiguous characterization of degradation products (DPs) was reached while using the potential of ion mobility-mass spectrometry (IM-MS) for fragment ions filtering (HDMS^E) and the implementation of two-dimensional (2D) NMR experiments with the non-uniform sampling (NUS) method. We have demonstrated the potential of quantitative NMR (qNMR) for the estimation of low level DPs. Finally, in order to simultaneously monitor multi-samples, the contribution of partial least squares (PLS) regression was evaluated. Our methodology was tested on three indapamide forced degradation conditions (acidic, basic, and oxidative) and it could be easily transposed in the drug development field to assist in the interpretation of complex mixtures (stability studies, impurities profiling, and biotransformation screening).

Effect of Biochar on Microbial Growth: A Metabolomics and Bacteriological Investigation in E. coli

Biochar has been proposed as a soil amendment in agricultural applications due to its advantageous adsorptive properties, high porosity, and low cost. These properties allow biochar to retain soil nutrients, yet the effects of biochar on bacterial growth remain poorly understood. To examine how biochar influences microbial metabolism, Escherichia coli was grown in a complex, well-defined media and treated with either biochar or activated carbon. The concentration of metabolites in the media were then quantified at several time points using NMR spectroscopy. Several metabolites were immediately adsorbed by the char, including l-asparagine, l-glutamine, and l-arginine. However, we find that biochar quantitatively adsorbs less of these metabolic precursors when compared to activated carbon. Electron microscopy reveals differences in surface morphology after cell culture, suggesting that Escherichia coli can form biofilms on the surfaces of the biochar. An examination of significant compounds in the tricarboxylic acid cycle and glycolysis reveals that treatment with biochar is less disruptive than activated carbon throughout metabolism. While both biochar and activated carbon slowed growth compared to untreated media, Escherichia coli in biochar-treated media grew more efficiently, as indicated by a longer logarithmic growth phase and a higher final cell density. This work suggests that biochar can serve as a beneficial soil amendment while minimizing the impact on bacterial viability. In addition, the experiments identify a mechanism for biochar's effectiveness in soil conditioning and reveal how biochar can alter specific bacterial metabolic pathways.

An Improved Method for Measuring Absolute Metabolite Concentrations in Small Biofluid or Tissue Samples

The measurement of absolute metabolite concentrations in small samples remains a significant analytical challenge. This is particularly the case when the sample volume is only a few microliters or less and cannot be determined accurately via direct measurement. We previously developed volume determination with two standards (VDTS) as a method to address this challenge for biofluids. As a proof-of-principle, we applied VDTS to NMR spectra of polar metabolites in the hemolymph (blood) of the tiny yet powerful genetic model Drosophila melanogaster. This showed that VDTS calculation of absolute metabolite concentrations in fed versus starved Drosophila larvae is more accurate than methods utilizing normalization to total spectral signal. Here, we introduce paired VDTS (pVDTS), an improved VDTS method for biofluids and solid tissues that implements the statistical power of paired control and experimental replicates. pVDTS utilizes new equations that also include a correction for dilution errors introduced by the variable surface wetness of solid samples. We then show that metabolite concentrations in Drosophila larvae are more precisely determined and logically consistent using pVDTS than using the original VDTS method. The refined pVDTS workflow described in this study is applicable to a wide range of different tissues and biofluids.

Real-Time Pure Shift HSQC NMR for Untargeted Metabolomics

Sensitivity and resolution are key considerations for NMR applications in general and for metabolomics in particular, where complex mixtures containing hundreds of metabolites over a large range of concentrations are commonly encountered. There is a strong demand for advanced methods that can provide maximal information in the shortest possible time frame. Here, we present the optimization and application of the recently introduced 2D real-time BIRD 1H-13C HSQC experiment for NMR-based metabolomics of aqueous samples at 13C natural abundance. For mouse urine samples, it is demonstrated how this real-time pure shift sensitivity-improved heteronuclear single quantum correlation method provides broadband homonuclear decoupling along the proton detection dimension and thereby significantly improves spectral resolution in regions that are affected by spectral overlap. Moreover, the collapse of the scalar multiplet structure of cross-peaks leads to a sensitivity gain of about 40-50% over a traditional 2D HSQC-SI experiment. The experiment works well over a range of magnetic field strengths and is particularly useful when resonance overlap in crowded regions of the HSQC spectra hampers accurate metabolite identification and quantitation.

Pharmacognosy in the digital era: shifting to contextualized metabolomics

Humans have co-evolved alongside numerous other organisms, some having a profound effect on health and nutrition. As the earliest pharmaceutical subject, pharmacognosy has evolved into a meta-discipline devoted to natural biomedical agents and their functional properties. While the acquisition of expanding data volumes is ongoing, contextualization is lagging. Thus, we assert that the establishment of an integrated and open databases ecosystem will nurture the discipline. After proposing an epistemological framework of knowledge acquisition in pharmacognosy, this study focuses on recent computational and analytical approaches. It then elaborates on the flux of research data, where good practices could foster the implementation of more integrated systems, which will in turn help shaping the future of pharmacognosy and determine its constitutional societal relevance.

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.

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.

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.

Deconvoluting interrelationships between concentrations and chemical shifts in urine provides a powerful analysis tool

The NMR chemical shifts of a substance in a complex mixture strongly depend on the composition of the mixture itself, as many weak interactions occur that are hardly predictable. Chemical shift variability is the major obstacle to automatically assigning, and subsequently quantitating, metabolite signals in body fluids, particularly urine. Here we demonstrate that the chemical shifts of signals in urine are actually predictable. This is achieved by constructing ca. 4000 artificial mixtures where the concentrations of 52 most abundant urine metabolites-including 11 inorganic ions-are varied, to sparsely but efficiently populate an N-dimensional concentration matrix. A strong relationship is established between the concentration matrix and the chemical shift matrix, so that chemical shifts of > 90 metabolite signals can be accurately predicted in real urine samples. The concentrations of the invisible inorganic ions are also accurately predicted, along with those of albumin and of several other abundant urine components.

Perdeuterated and 13C-enriched myo-inositol for DNP assisted monitoring of enzymatic phosphorylation by inositol-3-kinase

The synthesis of perdeuterated and 13C enriched myo-inositol is presented. Myo-inositol and its derivatives are of interest as substrates for enzymes producing phosphorylated species with regulatory functions in many organisms. Its utility in monitoring real-time phosphorylation by myo-inositol-3-kinase is illustrated using dynamic nuclear polarization (DNP) to enhance NMR observation.

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

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.

Epimetabolites: discovering metabolism beyond building and burning

Enzymatic transformations of primary, canonical metabolites generate active biomolecules that regulate important cellular and physiological processes. Roles include regulation of histone demethylation in epigenetics, inflammation in tissue injury, insulin sensitivity, cancer cell invasion, stem cell pluripotency status, inhibition of nitric oxide signaling and others. Such modified compounds, defined as epimetabolites, have functions distinct from classic hormones as well as removed from generic anabolism and catabolism. Epimetabolites are discovered by untargeted metabolomics using liquid- or gas chromatography-high resolution mass spectrometry and structurally annotated by in-silico fragmentation prediction tools. Their specific biological functions are subsequently investigated by targeted metabolomics methods.

How close are we to complete annotation of metabolomes?

The metabolome describes the full complement of the tens to hundreds of thousands of low molecular weight metabolites present within a biological system. Identification of the metabolome is critical for discovering the maximum amount of biochemical knowledge from metabolomics datasets. Yet no exhaustive experimental characterisation of any organismal metabolome has been reported to date, dramatically contrasting with the genome sequencing of thousands of plants, animals and microbes. Here, we review the status of metabolome annotation and describe advances in the analytical methodologies being applied. In part through new international coordination, we conclude that we are now entering a new era of metabolome annotation.

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.