Recent Advances in NMR-Based Metabolomics

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.

Gallic acid ameliorates synovial inflammation and fibrosis by regulating the intestinal flora and its metabolites

Gallic acid (GA) has been found by a large number of studies to have pharmacological effects such as antioxidant and anti-inflammatory properties. However, the underlying therapeutic mechanisms are not fully understood.. Studies have shown that altering the intestinal flora affects host metabolism and effectively mediates the development of synovitis. The aim of this study was to explore the pharmacological effects of GA in the treatment of synovial inflammation and anti-synovial fibrosis in knee osteoarthritis (KOA) and the underlying mechanisms by macrogenomics combined with off-target metabolomics. We established a synovitis model via in vivo and in vitro experiments to observe the effect of GA intervention on synovitis. Moreover, we collected serum and feces from rats and analyzed the changes in intestinal flora by macro-genome sequencing and the changes in metabolites in the serum by untargeted metabolomics. We found that GA reduced the levels of IL-1β, IL-6, and TNF-α, and decreased the protein expression levels of α-SMA, TGF-β, and Collagen I in synovial tissues and cells, and the composition and function of the intestinal flora were similarly altered. Combined with macrogenomic pathway enrichment analysis and metabolic pathway enrichment analysis, these findings revealed that GA impacts Bacteroidia and Muribaculaceae abundance, and via the following metabolic pathways: sphingolipid metabolism, glycerophospholipid metabolism, and arginine biology.to ameliorate synovial inflammation and fibrosis in KOA. The therapeutic effect of GA on KOA synovitis and fibrosis is partly attributed to the alleviation of metabolic disorder and the rebalancing of the intestinal flora. These results provides a rationale for the therapeutic application of GA in the treatment of synovitis.

Circadian rhythm in turbot (Scophthalmus maximus): daily variation of blood metabolites in recirculating aquaculture systems

INTRODUCTION

Animal welfare in aquaculture is becoming increasingly important, and detailed knowledge of the species concerned is essential for further optimization on farms. Every organism is controlled by an internal clock, the circadian rhythm, which is crucial for metabolic processes and is partially influenced by abiotic factors, making it important for aquaculture practices.

OBJECTIVE

In order to determine the circadian rhythm of adult turbot (Scophthalmus maximus), blood samples were collected over a 24-h period and plasma metabolite profiles were analyzed by 1H-NMR spectroscopy.

METHODS

The fish were habituated to feeding times at 9 am and 3 pm and with the NMR spectroscopy 46 metabolites could be identified, eight of which appeared to shift throughout the day.

RESULTS

We noted exceptionally high values around 3 pm for the amino acids isoleucine, leucine, valine, phenylalanine, lysine, and the stress indicator lactate. These metabolic peaks were interpreted as either habituation to the usual feeding time or as natural peak levels in turbot in a 24-h circle because other indicators for stress (glucose, cortisol and lysozymes) showed a stable baseline, indicating that the animals had no or very little stress during the experimental period.

CONCLUSION

This study provides initial insights into the diurnal variation of metabolites in adult turbot; however, further studies are needed to confirm present findings of possible fluctuations in amino acids and sugars. Implementing optimized feeding times (with high levels of sugars and low levels of stress metabolites) could lead to less stress, fewer disease outbreaks and overall improved fish welfare in aquaculture facilities.

1H NMR-based metabolomics study of the lipid profile of omega-3 fatty acid supplements and some vegetable oils

Omega-3 fatty acids, which consist of alpha-linolenic acid (ALA), docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA), are a type of polyunsaturated fatty acids that are crucial for enhancing human health. These three omega-3s are regarded as vital dietary nutrients because it cannot synthesize them on its own. Therefore, they must be obtained through dietary sources. On the other hands, there are concerns regarding the quality and quantity of omega-3 in dietary sources and supplements. In this study, 1H NMR spectroscopy and multivariate analysis were harnessed for non-destructive profiling of fatty acids in commercially available omega-3 supplements and plant-based oils. Results disclosed substantial disparities in omega-3 content from 8 to over 50 %, with some revealing unexpected adulteration. Notably, in one fish oil sample DHA could not be detected indicating the possibility of adulteration. Additionally, the research delineated the fatty acid composition of plant-based oils, emphasizing variations in alpha-linolenic acid (ALA) and linoleic acid (LA) content among flaxseed, chia seed, and walnut oils. Chia seeds emerged as a rich source of ALA (57-65 %mol), while walnuts contained considerable LA (44-53 % mol). The findings emphasize the power of metabolomics as a rapid and affordable tool for finding omega-3 fatty acids content and adulteration in commercial dietary products.

The longitudinal biochemical profiling of TBI in a drop weight model of TBI

Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide, particularly among individuals under the age of 45. It is a complex, and heterogeneous disease with a multifaceted pathophysiology that remains to be elucidated. Metabolomics has the potential to identify metabolic pathways and unique biochemical profiles associated with TBI. Herein, we employed a longitudinal metabolomics approach to study TBI in a weight drop mouse model to reveal metabolic changes associated with TBI pathogenesis, severity, and secondary injury. Using proton nuclear magnetic resonance (1H NMR) spectroscopy, we biochemically profiled post-mortem brain from mice that suffered mild TBI (N = 25; 13 male and 12 female), severe TBI (N = 24; 11 male and 13 female) and sham controls (N = 16; 11 male and 5 female) at baseline, day 1 and day 7 following the injury. 1H NMR-based metabolomics, in combination with bioinformatic analyses, highlights a few significant metabolites associated with TBI severity and perturbed metabolism related to the injury. We report that the concentrations of taurine, creatinine, adenine, dimethylamine, histidine, N-Acetyl aspartate, and glucose 1-phosphate are all associated with TBI severity. Longitudinal metabolic observation of brain tissue revealed that mild TBI and severe TBI lead distinct metabolic profile changes. A multi-class model was able to classify the severity of injury as well as time after TBI with estimated 86% accuracy. Further, we identified a high degree of correlation between respective hemisphere metabolic profiles (r > 0.84, p < 0.05, Pearson correlation). This study highlights the metabolic changes associated with underlying TBI severity and secondary injury. While comprehensive, future studies should investigate whether: (a) the biochemical pathways highlighted here are recapitulated in the brain of TBI sufferers and (b) if the panel of biomarkers are also as effective in less invasively harvested biomatrices, for objective and rapid identification of TBI severity and prognosis.

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

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

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy.

Integrated Digital Microfluidics NMR Spectroscopy: A Key Step toward Automated In Vivo Metabolomics

Toxicity testing is currently undergoing a paradigm shift from examining apical end points such as death, to monitoring sub-lethal toxicity in vivo. In vivo nuclear magnetic resonance (NMR) spectroscopy is a key platform in this endeavor. A proof-of-principle study is presented which directly interfaces NMR with digital microfluidics (DMF). DMF is a "lab on a chip" method allowing for the movement, mixing, splitting, and dispensing of μL-sized droplets. The goal is for DMF to supply oxygenated water to keep the organisms alive while NMR detects metabolomic changes. Here, both vertical and horizontal NMR coil configurations are compared. While a horizontal configuration is ideal for DMF, NMR performance was found to be sub-par and instead, a vertical-optimized single-sided stripline showed most promise. In this configuration, three organisms were monitored in vivo using ¹H-¹³C 2D NMR. Without support from DMF droplet exchange, the organisms quickly showed signs of anoxic stress; however, with droplet exchange, this was completely suppressed. The results demonstrate that DMF can be used to maintain living organisms and holds potential for automated exposures in future. However, due to numerous limitations of vertically orientated DMF, along with space limitations in standard bore NMR spectrometers, we recommend future development be performed using a horizontal (MRI style) magnet which would eliminate practically all the drawbacks identified here.

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

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

Metabolomics-Based Mechanistic Insights into Revealing the Adverse Effects of Pesticides on Plants: An Interactive Review

In plant biology, metabolomics is often used to quantitatively assess small molecules, metabolites, and their intermediates in plants. Metabolomics has frequently been applied to detect metabolic alterations in plants exposed to various biotic and abiotic stresses, including pesticides. The widespread use of pesticides and agrochemicals in intensive crop production systems is a serious threat to the functionality and sustainability of agroecosystems. Pesticide accumulation in soil may disrupt soil-plant relationships, thereby posing a pollution risk to agricultural output. Application of metabolomic techniques in the assessment of the biological consequences of pesticides at the molecular level has emerged as a crucial technique in exposome investigations. State-of-the-art metabolomic approaches such as GC-MS, LC-MS/MS UHPLC, UPLC-IMS-QToF, GC/EI/MS, MALDI-TOF MS, and ¹H-HR-MAS NMR, etc., investigating the harmful effects of agricultural pesticides have been reviewed. This updated review seeks to outline the key uses of metabolomics related to the evaluation of the toxicological impacts of pesticides on agronomically important crops in exposome assays as well as bench-scale studies. Overall, this review describes the potential uses of metabolomics as a method for evaluating the safety of agricultural chemicals for regulatory applications. Additionally, the most recent developments in metabolomic tools applied to pesticide toxicology and also the difficulties in utilizing this approach are discussed.

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.

Quantitative NMR Methods in Metabolomics

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the two major analytical platforms in the field of metabolomics, the other being mass spectrometry (MS). NMR is less sensitive than MS and hence it detects a relatively small number of metabolites. However, NMR exhibits numerous unique characteristics including its high reproducibility and non-destructive nature, its ability to identify unknown metabolites definitively, and its capabilities to obtain absolute concentrations of all detected metabolites, sometimes even without an internal standard. These characteristics outweigh the relatively low sensitivity and resolution of NMR in metabolomics applications. Since biological mixtures are highly complex, increased demand for new methods to improve detection, better identify unknown metabolites, and provide more accurate quantitation continues unabated. Technological and methodological advances to date have helped to improve the resolution and sensitivity and detection of a larger number of metabolite signals. Efforts focused on measuring unknown metabolite signals have resulted in the identification and quantitation of an expanded pool of metabolites including labile metabolites such as cellular redox coenzymes, energy coenzymes, and antioxidants. This chapter describes quantitative NMR methods in metabolomics with an emphasis on recent methodological developments, while highlighting the benefits and challenges of NMR-based metabolomics.

Microbiome and Metabolomics in Liver Cancer: Scientific Technology

Primary liver cancer is a heterogeneous disease. Liver cancer metabolism includes both the reprogramming of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment and fluctuations in regular tissue metabolism. Currently, metabolomics and metabolite profiling in liver cirrhosis, liver cancer, and hepatocellular carcinoma (HCC) have been in the spotlight in terms of cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecules, chemicals, and metabolites. Metabolomics technologies can provide critical information about the liver cancer state. Here, we review how liver cirrhosis, liver cancer, and HCC therapies interact with metabolism at the cellular and systemic levels. An overview of liver metabolomics is provided, with a focus on currently available technologies and how they have been used in clinical and translational research. We also list scalable methods, including chemometrics, followed by pathway processing in liver cancer. We conclude that important drivers of metabolomics science and scientific technologies are novel therapeutic tools and liver cancer biomarker analysis.

Metabolomic Signatures in Doxorubicin-Induced Metabolites Characterization, Metabolic Inhibition, and Signaling Pathway Mechanisms in Colon Cancer HCT116 Cells

Doxorubicin (DOX) is a chemotherapeutic agent is used for various cancer cells. To characterize the chemical structural components and metabolic inhibition, we applied a DOX to HCT116 colon cancer cells using an independent metabolites profiling approach. Chemical metabolomics has been involved in the new drug delivery systems. Metabolomics profiling of DOX-applied HCT116 colon cancer cellular metabolisms is rare. We used 1H nuclear magnetic resonance (NMR) spectroscopy in this study to clarify how DOX exposure affected HCT116 colon cancer cells. Metabolomics profiling in HCT116 cells detects 50 metabolites. Tracking metabolites can reveal pathway activities. HCT116 colon cancer cells were evenly treated with different concentrations of DOX for 24 h. The endogenous metabolites were identified by comparison with healthy cells. We found that acetate, glucose, glutamate, glutamine, sn-glycero-3-phosphocholine, valine, methionine, and isoleucine were increased. Metabolic expression of alanine, choline, fumarate, taurine, o-phosphocholine, inosine, lysine, and phenylalanine was decreased in HCT116 cancer cells. The metabolic phenotypic expression is markedly altered during a high dose of DOX. It is the first time that there is a metabolite pool and phenotypic expression in colon cancer cells. Targeting the DOX-metabolite axis may be a novel strategy for improving the curative effect of DOX-based therapy for colon cancer cells. These methods facilitate the routine metabolomic analysis of cancer cells.

Combining NMR and MS to Describe Pyrrole-2-Carbaldehydes in Wheat Bran of Radiation

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are indispensable analytical tools to provide chemical fingerprints in metabolomics studies. The present study evaluated radiation breeding wheat lines for chemical changes by non-targeted NMR-based metabolomics analysis of bran extracts. Multivariate analysis following spectral binning suggested pyrrole-2-carbaldehydes as chemical markers of four mutant lines with distinct NMR fingerprints in a δ_H range of 9.28-9.40 ppm. Further NMR and MS data analysis, along with chromatographic fractionation and synthetic preparation, aimed at structure identification of marker metabolites and identified five pyrrole-2-carbaldehydes. Quantum-mechanical driven ¹H iterative full spin analysis (QM-HiFSA) on synthetic pyrrole-2-carbaldehydes provided a precise description of complex peak patterns. Biological evaluation of pyrrole-2-carbaldehydes was performed with nine synthetic products, and six compounds showed hepatoprotective effects via modulation of reactive oxygen species production. Given that three out of five identified in wheat bran of radiation were described for hepatoprotective activity, the value of radiation mutation to greatly enhance pyrrole-2-carbaldehyde production was supported.

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted ¹³C and ¹H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted ¹³C and ¹H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of ¹H and ¹³C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

Bioactive Compounds from Marine Sponges and Algae: Effects on Cancer Cell Metabolome and Chemical Structures

Metabolomics represent the set of small organic molecules generally called metabolites, which are located within cells, tissues or organisms. This new “omic” technology, together with other similar technologies (genomics, transcriptomics and proteomics) is becoming a widely used tool in cancer research, aiming at the understanding of global biology systems in their physiologic or altered conditions. Cancer is among the most alarming human diseases and it causes a considerable number of deaths each year. Cancer research is one of the most important fields in life sciences. In fact, several scientific advances have been made in recent years, aiming to illuminate the metabolism of cancer cells, which is different from that of healthy cells, as suggested by Otto Warburg in the 1950s. Studies on sponges and algae revealed that these organisms are the main sources of the marine bioactive compounds involved in drug discovery for cancer treatment and prevention. In this review, we analyzed these two promising groups of marine organisms to focus on new metabolomics approaches for the study of metabolic changes in cancer cell lines treated with chemical extracts from sponges and algae, and for the classification of the chemical structures of bioactive compounds that may potentially prove useful for specific biotechnological applications.

Metabolomics and Chemoinformatics in Agricultural Biotechnology Research: Complementary Probes in Unravelling New Metabolites for Crop Improvement

Simple Summary

The world is facing an overarching threat to food security, particularly in developing nations. The issue is further exacerbated by the apparent impacts of biotic and abiotic stresses driving down crop yields and productivity. Conventional strategies to improve yields and sustain productivity have been employed, including plant breeding for favourable and resilient agronomic traits. However, the efficacy and success rates of these methods are declining, partly due to the rapid changes in climate variability and the emergence of new and resistant phytopathogens. Additionally, the process of creating new and improved transgenic varieties of crops is long and can be expensive. Thus, new and innovative technologies are required for crop improvement. This review explores recent advances in the science of metabolomics and chemoinformatics, which have presented an avenue for rapid and robust analysis; moreover, it explores the elucidation of the complex plant metabolome, providing the opportunity to decipher the reactionary mechanisms of plants to the surrounding environment through their metabolic activity. As such, specific metabolites can, thus, be selected as biomarkers for crop improvement based on their functional characteristics under varying environmental conditions (growth, development, and defence). This new knowledge can enhance breeding practices through rapid and robust metabolic engineering techniques for sustainable agriculture.

Abstract

The United Nations (UN) estimate that the global population will reach 10 billion people by 2050. These projections have placed the agroeconomic industry under immense pressure to meet the growing demand for food and maintain global food security. However, factors associated with climate variability and the emergence of virulent plant pathogens and pests pose a considerable threat to meeting these demands. Advanced crop improvement strategies are required to circumvent the deleterious effects of biotic and abiotic stress and improve yields. Metabolomics is an emerging field in the omics pipeline and systems biology concerned with the quantitative and qualitative analysis of metabolites from a biological specimen under specified conditions. In the past few decades, metabolomics techniques have been extensively used to decipher and describe the metabolic networks associated with plant growth and development and the response and adaptation to biotic and abiotic stress. In recent years, metabolomics technologies, particularly plant metabolomics, have expanded to screening metabolic biomarkers for enhanced performance in yield and stress tolerance for metabolomics-assisted breeding. This review explores the recent advances in the application of metabolomics in agricultural biotechnology for biomarker discovery and the identification of new metabolites for crop improvement. We describe the basic plant metabolomics workflow, the essential analytical techniques, and the power of these combined analytical techniques with chemometrics and chemoinformatics tools. Furthermore, there are mentions of integrated omics systems for metabolomics-assisted breeding and of current applications.

Metabolomics as a valid analytical technique in environmental exposure research: application and progress

BACKGROUND

In recent years, studies have shown that exposure to environmental pollutants (e.g., radiation, heavy metal substances, air pollutants, organic pollutants) is a leading cause of human non-communicable diseases. The key to disease prevention is to clarify the harmful mechanisms and toxic effects of environmental pollutants on the body. Metabolomics is a high-sensitivity, high-throughput omics technology that can obtain detailed metabolite information of an organism. It is a crucial tool for gaining a comprehensive understanding of the pathway network regulation mechanism of the organism. Its application is widespread in many research fields such as environmental exposure assessment, medicine, systems biology, and biomarker discovery.

AIM OF REVIEW

Recent findings show that metabolomics can be used to obtain molecular snapshots of organisms after environmental exposure, to help understand the interaction between environmental exposure and organisms, and to identify potential biomarkers and biological mechanisms.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review focuses on the application of metabolomics to understand the biological effects of radiation, heavy metals, air pollution, and persistent organic pollutants exposure, and examines some potential biomarkers and toxicity mechanisms.

Toward Nanomolar Multi-Component Analysis by 19F NMR

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

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

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

Comprehensive two-dimensional gas chromatography with mass spectrometry: an advanced bioanalytical technique for clinical metabolomics studies

This review highlights the current state of knowledge in the development of GC × GC-MS for the analysis of clinical metabolites. Selected applications are described as well as our perspectives on current challenges and potential future directions.

Geographical Origin Assessment of Extra Virgin Olive Oil via NMR and MS Combined with Chemometrics as Analytical Approaches

Geographical origin assessment of extra virgin olive oil (EVOO) is recognised worldwide as raising consumers’ awareness of product authenticity and the need to protect top-quality products. The need for geographical origin assessment is also related to mandatory legislation and/or the obligations of true labelling in some countries. Nevertheless, official methods for such specific authentication of EVOOs are still missing. Among the analytical techniques useful for certification of geographical origin, nuclear magnetic resonance (NMR) and mass spectroscopy (MS), combined with chemometrics, have been widely used. This review considers published works describing the use of these analytical methods, supported by statistical protocols such as multivariate analysis (MVA), for EVOO origin assessment. The research has shown that some specific countries, generally corresponding to the main worldwide producers, are more interested than others in origin assessment and certification. Some specific producers such as Italian EVOO producers may have been focused on this area because of consumers’ interest and/or intrinsic economical value, as testified also by the national concern on the topic. Both NMR- and MS-based approaches represent a mature field where a general validation method for EVOOs geographic origin assessment could be established as a reference recognised procedure.

A drug discovery toolbox for Nuclear Magnetic Resonance (NMR) characterization of ligands and their targets

Information about the structure, dynamics, and ligand-binding properties of biomolecules can be derived from Nuclear Magnetic Resonance (NMR) spectroscopy and provides valuable information for drug discovery. A multitude of experimental approaches provides a wealth of information that can be tailored to the system of interest. Methods to study the behavior of ligands upon target binding enable the identification of weak binders in a robust manner that is critical for the identification of truly novel binding interactions. This is particularly important for challenging targets. Observing the solution behavior of biomolecules yields information about their structure, dynamics, and interactions. This review describes the breadth of approaches that are available, many of which are under-utilized in a drug-discovery environment, and focuses on recent advances that continue to emerge.

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

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

1H Nuclear Magnetic Resonance Spectroscopy-Based Methods for the Quantification of Proteins in Urine

We described several postprocessing methods to measure protein concentrations in human urine from existing ¹H nuclear magnetic resonance (NMR) metabolomic spectra: (1) direct spectral integration, (2) integration of NCD spectra (NCD = 1D NOESY-CPMG), (3) integration of SMolESY-filtered 1D NOESY spectra (SMolESY = Small Molecule Enhancement SpectroscopY), (4) matching protein patterns, and (5) TSP line integral and TSP linewidth. Postprocessing consists of (a) removal of the metabolite signals (demetabolization) and (b) extraction of the protein integral from the demetabolized spectra. For demetabolization, we tested subtraction of the spin-echo 1D spectrum (CPMG) from the regular 1D spectrum and low-pass filtering of 1D NOESY by its derivatives (c-SMolESY). Because of imperfections in the demetabolization, in addition to direct integration, we extracted protein integrals by the piecewise comparison of demetabolized spectra with the reference spectrum of albumin. We analyzed 42 urine samples with protein content known from the bicinchoninic acid (BCA) assay. We found excellent correlation between the BCA assay and the demetabolized NMR integrals. We have provided conversion factors for calculating protein concentrations in mg/mL from spectral integrals in mM. Additionally, we found the trimethylsilyl propionate (TSP, NMR standard) spectral linewidth and the TSP integral to be good indicators of protein concentration. The described methods increase the information content of urine NMR metabolomics spectra by informing clinical studies of protein concentration.

A study using LC-MS/MS-based metabolomics to investigate the effects of iron oxide nanoparticles on rat liver

Iron oxide nanoparticles (IONPs) are widely used in food additives, but their metabolic mechanism in the body is still unclear. In this study, male Sprague-Dawley rats were orally administered with IONPs for 28 days to investigate the adverse effect and metabolic mechanism on liver by the combination of traditional toxicology technology and liquid chromatography tandem-mass spectrometry (LC-MS/MS)-based metabolomics. The results showed that IONPs could increase the concentration of blood glucose and the metabolites in the liver of the control and IONPs-treated group were significantly changed. A total of 32 different metabolites were found, including choline, Phosphatidylcholine (PC), Phosphatidylethanolamine (PE), Phosphatidylserine (PS), etc. Pathway analysis based on KEGG database demonstrated that the glycerophospholipid metabolism pathway would be affected. And the expression of the key enzymes of altered metabolomics pathway was further verified at the transcription level. In short, our study clarified oral exposure to IONPs would induce lipid metabolism disorders in the liver of rats, which provided useful information about their safety and potential risks.

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.

Cerebrospinal fluid metabolomics: detection of neuroinflammation in human central nervous system disease

The high morbidity and mortality of neuroinflammatory diseases drives significant interest in understanding the underlying mechanisms involved in the innate and adaptive immune response of the central nervous system (CNS). Diagnostic biomarkers are important to define treatable neuroinflammation. Metabolomics is a rapidly evolving research area offering novel insights into metabolic pathways, and elucidation of reliable metabolites as biomarkers for diseases. This review focuses on the emerging literature regarding the detection of neuroinflammation using cerebrospinal fluid (CSF) metabolomics in human cohort studies. Studies of classic neuroinflammatory disorders such as encephalitis, CNS infection and multiple sclerosis confirm the utility of CSF metabolomics. Additionally, studies in neurodegeneration and neuropsychiatry support the emerging potential of CSF metabolomics to detect neuroinflammation in common CNS diseases such as Alzheimer's disease and depression. We demonstrate metabolites in the tryptophan-kynurenine pathway, nitric oxide pathway, neopterin and major lipid species show moderately consistent ability to differentiate patients with neuroinflammation from controls. Integration of CSF metabolomics into clinical practice is warranted to improve recognition and treatment of neuroinflammation.

Phytotoxicity of Schiekia timida Seed Extracts, a Mixture of Phenylphenalenones

Phenylphenalenones, metabolites found in Schiekia timida (Haemodoraceae), are a class of specialized metabolites with many biological activities, being phytoalexins in banana plants. In the constant search to solve the problem of glyphosate and to avoid resistance to commercial herbicides, this work aimed to investigate the phytotoxic effect of the methanolic extract of S. timida seeds. The chemical composition of the seed extract was directly investigated by NMR and UPLC-QToF MS and the pre- and post-emergence phytotoxic effect on a eudicotyledonous model (Lactuca sativa) and a monocotyledonous model (Allium cepa) was evaluated through germination and seedling growth tests. Three concentrations of the extract (0.25, 0.50, and 1.00 mg/mL) were prepared, and four replicates for each of them were analyzed. Three major phenylphenalenones were identified by NMR spectroscopy: 4-hydroxy-anigorufone, methoxyanigorufone, and anigorufone, two of those reported for the first time in S. timida. The presence of seven other phenylphenalenones was suggested by the LC-MS analyses. The phenylphenalenone mixture did not affect the germination rate, but impaired radicle and hypocotyl growth on both models. The effect in the monocotyledonous model was statistically similar to glyphosate in the lowest concentration (0.25 mg/mL). Therefore, although more research on this topic is required to probe this first report, this investigation suggests for the first time that phenylphenalenone compounds may be post-emergence herbicides.

Investigation on the stability in plant metabolomics with a special focus on freeze-thaw cycles: LC-MS and NMR analysis to Cassiae Semen (Cassia obtusifolia L.) seeds as a case study

Metabolomics is a rapid and sensitive tool for the detection of dynamic metabolic compositions in the study of systemic metabolic consequences. However, it is also susceptible to a tiny variation of pre-analytical handling procedures. To provide reproducible results, specific knowledge on metabolites perturbance along with different freeze-thaw cycles (FTCs) is needed for further metabolomics studies. In this paper, five FTCs of germinated Cassiae Semen (CS) were chosen as a case study to investigate the influence of FTC effect based on UHPLC-Q-Orbitrap-MS and NMR technologies. A total of 108 metabolites were relatively quantified by LC-MS and NMR analyses. Principal component analysis (PCA) showed that the first and second FTC samples are welly separated from the other groups; however, the extent of FTC-induced effects are smaller after the third cycle. Upon five consecutive FTCs, alterations which consisted of decreased stachyose, sucrose, norrubrofusarin-6-O-β-d-glucopyranoside, and quercetin 3-(3″-acetylgalactoside), as well as increased phenylalanine, leucine, isoleucine, methionine, phenylalanine, mannose, gluconic acid, and valine, could be observed. FTC does not exert the same effect on all metabolites. Although a large number of secondary metabolites were stable when subjected to five FTCs, FTC effects may lead to false-positive in the discovery of biomarker. In the case of reusing plant seed samples, no more than three consecutive freeze-thaw cycles were found advisable. This work provides unique perspectives on the FTC effects, which may fill in some existing gaps in the knowledge of the stability of plant metabolites during sample pre-handling.

Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research

Ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS) variants currently represent the best tools to tackle the challenges of complexity and lack of comprehensive coverage of the metabolome. UHPLC offers flexible and efficient separation coupled with high-sensitivity detection via HRMS, allowing for the detection and identification of a broad range of metabolites. Here we discuss current common strategies for UHPLC-HRMS-based metabolomics, with a focus on expanding metabolome coverage.

Detection of Metabolite-Protein Interactions in Complex Biological Samples by High-Resolution Relaxometry: Toward Interactomics by NMR

Metabolomics, the systematic investigation of metabolites in biological fluids, cells, or tissues, reveals essential information about metabolism and diseases. Metabolites have functional roles in a myriad of biological processes, as substrates and products of enzymatic reactions but also as cofactors and regulators of large numbers of biochemical mechanisms. These functions involve interactions of metabolites with macromolecules. Yet, methods to systematically investigate these interactions are still scarce to date. In particular, there is a need for techniques suited to identify and characterize weak metabolite-macromolecule interactions directly in complex media such as biological fluids. Here, we introduce a method to investigate weak interactions between metabolites and macromolecules in biological fluids. Our approach is based on high-resolution NMR relaxometry and does not require any invasive procedure or separation step. We show that we can detect interactions between small and large molecules in human blood serum and quantify the size of the complex. Our work opens the way for investigations of metabolite (or other small molecules)-protein interactions in biological fluids for interactomics or pharmaceutical applications.

NMR Analysis of Carboxylate Isotopomers of 13C-Metabolites by Chemoselective Derivatization with 15N-Cholamine

A substantial fraction of common metabolites contains carboxyl functional groups. Their ¹³C isotopomer analysis by nuclear magnetic resonance (NMR) is hampered by the low sensitivity of the ¹³C nucleus, the slow longitudinal relaxation for the lack of an attached proton, and the relatively low chemical shift dispersion of carboxylates. Chemoselective (CS) derivatization is a means of tagging compounds in a complex mixture via a specific functional group. ¹⁵N₁-cholamine has been shown to be a useful CS agent for carboxylates, producing a peptide bond that can be detected via ¹⁵N-attached H with high sensitivity in heteronuclear single quantum coherence experiments. Here, we report an improved method of derivatization and show how ¹³C-enrichment at the carboxylate and/or the adjacent carbon can be determined via one- and two-bond coupling of the carbons adjacent to the cholamine ¹⁵N atom in the derivatives. We have applied this method for the determination of ¹³C isotopomer distribution in the extracts of A549 cell culture and liver tissue from a patient-derived xenograft mouse.

Ethnobotany and the Role of Plant Natural Products in Antibiotic Drug Discovery

The crisis of antibiotic resistance necessitates creative and innovative approaches, from chemical identification and analysis to the assessment of bioactivity. Plant natural products (NPs) represent a promising source of antibacterial lead compounds that could help fill the drug discovery pipeline in response to the growing antibiotic resistance crisis. The major strength of plant NPs lies in their rich and unique chemodiversity, their worldwide distribution and ease of access, their various antibacterial modes of action, and the proven clinical effectiveness of plant extracts from which they are isolated. While many studies have tried to summarize NPs with antibacterial activities, a comprehensive review with rigorous selection criteria has never been performed. In this work, the literature from 2012 to 2019 was systematically reviewed to highlight plant-derived compounds with antibacterial activity by focusing on their growth inhibitory activity. A total of 459 compounds are included in this Review, of which 50.8% are phenolic derivatives, 26.6% are terpenoids, 5.7% are alkaloids, and 17% are classified as other metabolites. A selection of 183 compounds is further discussed regarding their antibacterial activity, biosynthesis, structure-activity relationship, mechanism of action, and potential as antibiotics. Emerging trends in the field of antibacterial drug discovery from plants are also discussed. This Review brings to the forefront key findings on the antibacterial potential of plant NPs for consideration in future antibiotic discovery and development efforts.

Metabolomics, metabolic flux analysis and cancer pharmacology

Metabolic reprogramming is a hallmark of cancer and increasing evidence suggests that reprogrammed cell metabolism supports tumor initiation, progression, metastasis and drug resistance. Understanding metabolic dysregulation may provide therapeutic targets and facilitate drug research and development for cancer therapy. Metabolomics enables the high-throughput characterization of a large scale of small molecule metabolites in cells, tissues and biofluids, while metabolic flux analysis (MFA) tracks dynamic metabolic activities using stable isotope tracer methods. Recent advances in metabolomics and MFA technologies make them powerful tools for metabolic profiling and characterizing metabolic activities in health and disease, especially in cancer research. In this review, we introduce recent advances in metabolomics and MFA analytical technologies, and provide the first comprehensive summary of the most commonly used isotope tracing methods. In addition, we highlight how metabolomics and MFA are applied in cancer pharmacology studies particularly for discovering targetable metabolic vulnerabilities, understanding the mechanisms of drug action and drug resistance, exploring potential strategies with dietary intervention, identifying cancer biomarkers, as well as enabling precision treatment with pharmacometabolomics.

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

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

Serum metabolomics analysis reveals that weight loss in obese dogs results in a similar metabolic profile to dogs in ideal body condition

INTRODUCTION

The study of metabolic profile can be an important tool to better understand, at a systemic level, metabolic alterations caused by different pathological conditions, such as obesity. Furthermore, it allows the discovery of metabolic biomarkers, which may help to diagnose alterations caused by obesity.

OBJECTIVE

To investigate the metabolic profile of blood serum of obese dogs, control dogs, and dogs that were subjected to a weight loss program.

METHODS

Ten obese adult spayed female dogs were included, and their body composition was determined by the deuterium isotope dilution method. The dogs were subjected to a weight loss program and formed a new experimental group after losing 20% of the initial body weight. A third experimental group was composed of ten lean adult spayed female dogs. The metabolic profile of blood serum was evaluated through nuclear magnetic resonance (NMR). Principal Component Analyses (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) models were constructed using Pareto scaling pre-processing. Pathway analysis was also performed using the MetaboAnalist online tool.

RESULTS

The PCA shows that the control and after weight loss groups presented a trend to negative PC1, indicating similarities between these two groups. In contrast, obese animals presented a tendency to appear on negative PC2 indicating a different metabolic profile. The OPLS-DA analysis of the serum indicated that healthy groups presented higher content of glucose, while animals that lost weight had higher levels of cholesterol and lactate than the control group. On the other hand, the analysis showed that lipid content, cholesterol, and branched-chain amino acids were highest in obese animals. Variable Influence on Projection (VIP) analysis demonstrated that Lactate is the most important metabolite for the OPLS-DA model and Hierarchical Cluster Analysis (HCA) corroborated the similarity between the control group and the obese after weight loss groups. Moreover, the pathway analysis indicated the most important metabolic pathways related to this dataset.

CONCLUSIONS

The metabolomic assessment based on NMR of blood serum differed between obese dogs and animals in optimal body condition. Moreover, the weight loss resulted in metabolic profiles similar to those observed in lean animals.

Evaluation of Fumaric Acid and Maleic Acid as Internal Standards for NMR Analysis of Protein Precipitated Plasma, Serum, and Whole Blood

Significant advances have been made in unknown metabolite identification and expansion of the number of quantifiable metabolites in human plasma, serum, and whole blood using NMR spectroscopy. However, reliable quantitation of metabolites is still a challenge. A major bottleneck is the lack of a suitable internal standard that does not interact with the complex blood sample matrix and also does not overlap with metabolite peaks apart from exhibiting other favorable characteristics. With the goal of addressing this challenge, a comprehensive investigation of fumaric and maleic acids as potential internal standards was made along with a comparison with the conventional standards, TSP (trimethylsilylpropionic acid) and DSS (trimethylsilylpropanesulfonic acid). Both fumaric acid and maleic acid exhibited a surprisingly high performance with a quantitation error <1%, while the TSP and DSS caused an average error of up to 35% in plasma, serum, and whole blood. Further, the results indicate that while fumaric acid is a robust standard for all three biospecimens, maleic acid is suitable for only plasma and serum. Maleic acid is not suited for the analysis of whole blood due to its overlap with coenzyme peaks. These findings provide new opportunities for improved and accurate quantitation of metabolites in human plasma, serum, and whole blood using NMR spectroscopy. Moreover, the use of protein precipitation prior to NMR analysis mirrors the sample preparation commonly used for mass spectrometry based metabolomics, such that these findings further strengthen efforts to combine and compare NMR and MS based metabolite data of human plasma, serum, and whole blood for metabolomics based research.

NMR-Based Metabolomics

Nuclear magnetic resonance (NMR) spectroscopy is a major analytical method used in the growing field of metabolomics. Although NMR is relatively less sensitive than mass spectrometry, this analytical platform has numerous characteristics including its high reproducibility and quantitative abilities, its nonselective and noninvasive nature, and the ability to identify unknown metabolites in complex mixtures and trace the downstream products of isotope labeled substrates ex vivo, in vivo, or in vitro. Metabolomic analysis of highly complex biological mixtures has benefitted from the advances in both NMR data acquisition and analysis methods. Although metabolomics applications span a wide range of disciplines, a majority has focused on understanding, preventing, diagnosing, and managing human diseases. This chapter describes NMR-based methods relevant to the rapidly expanding metabolomics field.

NMR spectroscopy analysis reveals differential metabolic responses in arabidopsis roots and leaves treated with a cytokinesis inhibitor

In plant cytokinesis, de novo formation of a cell plate evolving into the new cell wall partitions the cytoplasm of the dividing cell. In our earlier chemical genomics studies, we identified and characterized the small molecule endosidin-7, that specifically inhibits callose deposition at the cell plate, arresting late-stage cytokinesis in arabidopsis. Endosidin-7 has emerged as a very valuable tool for dissecting this essential plant process. To gain insights regarding its mode of action and the effects of cytokinesis inhibition on the overall plant response, we investigated the effect of endosidin-7 through a nuclear magnetic resonance spectroscopy (NMR) metabolomics approach. In this case study, metabolomics profiles of arabidopsis leaf and root tissues were analyzed at different growth stages and endosidin-7 exposure levels. The results show leaf and root-specific metabolic profile changes and the effects of endosidin-7 treatment on these metabolomes. Statistical analyses indicated that the effect of endosidin-7 treatment was more significant than the developmental impact. The endosidin-7 induced metabolic profiles suggest compensations for cytokinesis inhibition in central metabolism pathways. This study further shows that long-term treatment of endosidin-7 profoundly changes, likely via alteration of hormonal regulation, the primary metabolism of arabidopsis seedlings. Hormonal pathway-changes are likely reflecting the plant’s responses, compensating for the arrested cell division, which in turn are leading to global metabolite modulation. The presented NMR spectral data are made available through the Metabolomics Workbench, providing a reference resource for the scientific community.

Metabolomic insights into the lasting impacts of early-life exposure to BDE-47 in mice

Early-life exposure to toxicants may have lasting effects that adversely impact later development. Thus, although the production and use of a toxicant have been banned, the risk to previously exposed individuals may continue. BDE-47, a component of commercial penta-BDEs, is a persistent organic pollutant with demonstrated neurotoxicity. To investigate the persistent effects of BDE-47 and the mechanisms thereof, we employed a metabolomics approach to analyze the brain, blood and urine of mice exposed to BDE-47 for 28 days and then 3 months post-exposure. In the brain, BDE-47 was detectable just after exposure but was below the limit of detection (LOD) 3 months later. However, the metabolomic alterations caused by early-life exposure to BDE-47 persisted. Potential biomarkers related to these alterations included phosphatidylcholine, lysophosphatidylcholine, sphingomyelin and several amino acids and biogenic amines. The metabolic pathways involved in the response to BDE-47 in the brain were mainly those related to glycerophospholipid metabolism, sphingomyelin metabolism and neurotransmitter regulation. Thus, our study demonstrates the utility of metabolomics, as the omics most closely reflecting the phenotype, in exploring the mechanisms underlying the lasting effects induced by early-life BDE-47 exposure.

Staring into the void: demystifying microbial metabolomics

Metabolites give us a window into the chemistry of microbes and are split into two subclasses: primary and secondary. Primary metabolites are required for life whereas secondary metabolites have historically been classified as those appearing after exponential growth and are not necessarily needed for survival. Many microbial species are estimated to produce hundreds of metabolites and can be affected by differing nutrients. Using various analytical techniques, metabolites can be directly detected in order to elucidate their biological significance. Currently, a single experiment can produce anywhere from megabytes to terabytes of data. This big data has motivated scientists to develop informatics tools to help target specific metabolites or sets of metabolites. Broadly, it is imperative to identify clear biological questions before embarking on a study of metabolites (metabolomics). For instance, studying the effect of a transposon insertion on phenazine biosynthesis in Pseudomonas is a very different from asking what molecules are present in a specific banana-derived strain of Pseudomonas. This review is meant to serve as a primer for a 'choose your own adventure' approach for microbiologists with limited mass spectrometry expertise, with a strong focus on liquid chromatography mass spectrometry based workflows developed or optimized within the past five years.

Cannabinomics: Application of Metabolomics in Cannabis (Cannabis sativa L.) Research and Development

Cannabis (Cannabis sativa L.) is a complex, polymorphic plant species, which produces a vast array of bioactive metabolites, the two major chemical groups being cannabinoids and terpenoids. Nonetheless, the psychoactive cannabinoid tetrahydrocannabinol (Δ 9 -THC) and the non-psychoactive cannabidiol (CBD), are the two major cannabinoids that have monopolized the research interest. Currently, more than 600 Cannabis varieties are commercially available, providing access to a multitude of potent extracts with complex compositions, whose genetics are largely inconclusive. Recently introduced legislation on Cannabis cultivation in many countries represents a great opportunity, but at the same time, a great challenge for Cannabis research and development (R&D) toward applications in the pharmaceutical, food, cosmetics, and agrochemical industries. Based on its versatility and unique capabilities in the deconvolution of the metabolite composition of complex matrices, metabolomics represents an ideal bioanalytical tool that could greatly assist and accelerate Cannabis R&D. Among others, Cannabis metabolomics or cannabinomics can be applied in the taxonomy of Cannabis varieties in chemovars, the research on the discovery and assessment of new Cannabis-based sources of bioactivity in medicine, the development of new food products, and the optimization of its cultivation, aiming for improvements in yield and potency. Although Cannabis research is still in its infancy, it is highly foreseen that the employment of advanced metabolomics will provide insights that could assist the sector to face the aforementioned challenges. Within this context, here, the current state-of-the-art and conceptual aspects of cannabinomics are presented.

Distinguishing NASH Histological Severity Using a Multiplatform Metabolomics Approach

Nonalcoholic fatty liver disease (NAFLD) is categorized based on histological severity into nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH). We used a multiplatform metabolomics approach to identify metabolite markers and metabolic pathways that distinguish NAFL from early NASH and advanced NASH. We analyzed fasting serum samples from 57 prospectively-recruited patients with histologically-proven NAFLD, including 12 with NAFL, 31 with early NASH and 14 with advanced NASH. Metabolite profiling was performed using a combination of liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analyzed with multivariate statistical and pathway analysis tools. We targeted 237 metabolites of which 158 were quantified. Multivariate analysis uncovered metabolite profile clusters for patients with NAFL, early NASH, and advanced NASH. Also, multiple individual metabolites were associated with histological severity, most notably spermidine which was more than 2-fold lower in advanced fibrosis vs. early fibrosis, in advanced NASH vs. NAFL and in advanced NASH vs. early NASH, suggesting that spermidine exercises a protective effect against development of fibrosing NASH. Furthermore, the results also showed metabolic pathway perturbations between early-NASH and advanced-NASH. In conclusion, using a combination of two reliable analytical platforms (LC-MS and NMR spectroscopy) we identified individual metabolites, metabolite clusters and metabolic pathways that were significantly different between NAFL, early-NASH, and advanced-NASH. These differences provide mechanistic insights as well as potentially important metabolic biomarker candidates that may noninvasively distinguish patients with NAFL, early-NASH, and advanced-NASH. The associations of spermidine levels with less advanced histology merit further assessment of the potential protective effects of spermidine in NAFLD.

NMR-Based Plant Metabolomics in Nutraceutical Research: An Overview

Few topics are able to channel the interest of researchers, the public, and industries, like nutraceuticals. The ever-increasing demand of new compounds or new sources of known active compounds, along with the need of a better knowledge about their effectiveness, mode of action, safety, etc., led to a significant effort towards the development of analytical approaches able to answer the many questions related to this topic. Therefore, the application of cutting edges approaches to this area has been observed. Among these approaches, metabolomics is a key player. Herewith, the applications of NMR-based metabolomics to nutraceutical research are discussed: after a brief overview of the analytical workflow, the use of NMR-based metabolomics to the search for new compounds or new sources of known nutraceuticals are reviewed. Then, possible applications for quality control and nutraceutical optimization are suggested. Finally, the use of NMR-based metabolomics to study the impact of nutraceuticals on human metabolism is discussed.

The Protective Effect of Basic Fibroblast Growth Factor on Diabetic Nephropathy Through Remodeling Metabolic Phenotype and Suppressing Oxidative Stress in Mice

Diabetic nephropathy is a common complication in diabetes, but still lack of effective therapeutic strategies. This study aimed to investigate the therapeutic effect of basic fibroblast growth factor (bFGF) in db/db mice with diabetic nephropathy and explore its possible metabolic mechanisms using a nuclear magnetic resonance-based metabolomic approach. We found that bFGF treatment significantly alleviate urinary albumin to creatinine ratio and renal fibrosis in db/db mice, suggesting a potential renal protective effect. Metabolomics results reveal that bFGF remodeled metabolic phenotypes of the kidney and urine in db/db mice, mainly involving energy metabolism, methylamine metabolism, osmoregulation, and oxidative stress. Furthermore, the results show that bFGF-induced reductions of oxidative stress and apoptosis in db/db mice might be mediated by NOX-ROS-Nrf2 signaling. Therefore, our study suggests that the protective effect of bFGF on diabetic nephropathy could be mediated by remodeling metabolic phenotype and suppressing oxidative stress.