Can NMR solve some significant challenges in metabolomics?

Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor

Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (¹H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel ¹H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the ¹H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).

Maternal Rat Metabolomics: Amniotic Fluid and Placental Metabolic Profiling Workflows

Studying the metabolome of specific gestational compartments is of growing interest in the context of fetus developmental disorders. However, the metabolomes of the placenta and amniotic fluid (AF) are poorly characterized. Therefore, we present the validation of a fingerprinting methodology. Using pregnant rats, we performed exhaustive and robust extractions of metabolites in the AF and lipids and more polar metabolites in the placenta. For the AF, we compared the extraction capabilities of methanol (MeOH), acetonitrile (ACN), and a mixture of both. For the placenta, we compared (i) the extraction capabilities of dichloromethane, methyl t-butyl ether (MTBE), and butanol, along with (ii) the impact of lyophilization of the placental tissue. Analyses were performed on a C18 and hydrophilic interaction liquid chromatography combined with high-resolution mass spectrometry. The efficiency and the robustness of the extractions were compared based on the number of the features or metabolites (for untargeted or targeted approach, respectively), their mean total intensity, and their coefficient of variation (% CV). The extraction capabilities of MeOH and ACN on the AF metabolome were equivalent. Lyophilization also had no significant impact and usefulness on the placental tissue metabolome profiling. Considering the placental lipidome, MTBE extraction was more informative because it allowed extraction of a slightly higher number of lipids, in higher concentration. This proof-of-concept study assessing the metabolomics and lipidomics of the AF and the placenta revealed changes in both metabolisms, at two different stages of rat gestation, and allowed a detailed prenatal metabolic fingerprinting.

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.

Establishing a Metabolite Extraction Method to Study the Metabolome of Blastocystis Using NMR

Blastocystis is an opportunistic parasite commonly found in the intestines of humans and other animals. Despite its high prevalence, knowledge regarding Blastocystis biology within and outside the host is limited. Analysis of the metabolites produced by this anaerobe could provide insights that can help map its metabolism and determine its role in both health and disease. Due to its controversial pathogenicity, these metabolites could define its deterministic role in microbiome's "health" and/or subsequently resolve Blastocystis' potential impact in gastrointestinal health. A common method for elucidating the presence of these metabolites is through ¹H nuclear magnetic resonance (NMR). However, there are currently no described benchmarked methods available to extract metabolites from Blastocystis for ¹H NMR analysis. Herein, several extraction solvents, lysis methods and incubation temperatures were compared for their usefulness as an extraction protocol for this protozoan. Following extraction, the samples were freeze-dried, re-solubilized and analysed with ¹H NMR. The results demonstrate that carrying out the procedure at room temperature using methanol as an extraction solvent and bead bashing as a lysis technique provides a consistent, reproducible and efficient method to extract metabolites from Blastocystis for NMR.

Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine ¹H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.

NMR-Based Metabolomics in Cancer Research

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

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.

Magnetic resonance spectroscopy for the study of cns malignancies

Despite intensive research, brain tumors are amongst the malignancies with the worst prognosis; therefore, a prompt diagnosis and thoughtful assessment of the disease is required. The resistance of brain tumors to most forms of conventional therapy has led researchers to explore the underlying biology in search of new vulnerabilities and biomarkers. The unique metabolism of brain tumors represents one potential vulnerability and the basis for a system of classification. Profiling this aberrant metabolism requires a method to accurately measure and report differences in metabolite concentrations. Magnetic resonance-based techniques provide a framework for examining tumor tissue and the evolution of disease. Nuclear Magnetic Resonance (NMR) analysis of biofluids collected from patients suffering from brain cancer can provide biological information about disease status. In particular, urine and plasma can serve to monitor the evolution of disease through the changes observed in the metabolic profiles. Moreover, cerebrospinal fluid can be utilized as a direct reporter of cerebral activity since it carries the chemicals exchanged with the brain tissue and the tumor mass. Metabolic reprogramming has recently been included as one of the hallmarks of cancer. Accordingly, the metabolic rewiring experienced by these tumors to sustain rapid growth and proliferation can also serve as a potential therapeutic target. The combination of 13C tracing approaches with the utilization of different NMR spectral modalities has allowed investigations of the upregulation of glycolysis in the aggressive forms of brain tumors, including glioblastomas, and the discovery of the utilization of acetate as an alternative cellular fuel in brain metastasis and gliomas. One of the major contributions of magnetic resonance to the assessment of brain tumors has been the non-invasive determination of 2-hydroxyglutarate (2HG) in tumors harboring a mutation in isocitrate dehydrogenase 1 (IDH1). The mutational status of this enzyme already serves as a key feature in the clinical classification of brain neoplasia in routine clinical practice and pilot studies have established the use of in vivo magnetic resonance spectroscopy (MRS) for monitoring disease progression and treatment response in IDH mutant gliomas. However, the development of bespoke methods for 2HG detection by MRS has been required, and this has prevented the wider implementation of MRS methodology into the clinic. One of the main challenges for improving the management of the disease is to obtain an accurate insight into the response to treatment, so that the patient can be promptly diverted into a new therapy if resistant or maintained on the original therapy if responsive. The implementation of 13C hyperpolarized magnetic resonance spectroscopic imaging (MRSI) has allowed detection of changes in tumor metabolism associated with a treatment, and as such has been revealed as a remarkable tool for monitoring response to therapeutic strategies. In summary, the application of magnetic resonance-based methodologies to the diagnosis and management of brain tumor patients, in addition to its utilization in the investigation of its tumor-associated metabolic rewiring, is helping to unravel the biological basis of malignancies of the central nervous system.

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.

Chemometric techniques coupled with NMR for matabolic profiling of lettuce exposed to polycyclic aromatic hydrocarbones

Treated waste water (TWW) quality varies due to the occurrence of polycyclic aromatic hydrocarbons (PAHs) up to low μg L-1. In this study, a non-targeted metabolomic analysis was performed on lettuce (Lactuca sativa L) exposed to 4 PAHs by irrigation. The plants were watered with different concentrations of contaminants (0-100 μg L-1) for 39 days under controlled conditions and then harvested, extracted and analyzed by nuclear magnetic resonance (NMR). Different chemometric tools based on principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are proposed for the analysis of the complex data sets generated in the different exposure experiments. Furthermore, Analysis of Variance Simultaneous Component Analysis (ASCA) of changes on metabolite peaks showed significant PAHs concentration and exposure time-dependent changes, clearly differentiating between exposed and non-exposed samples.

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.

The Zebra Mussel (Dreissena polymorpha) as a Model Organism for Ecotoxicological Studies: A Prior 1H NMR Spectrum Interpretation of a Whole Body Extract for Metabolism Monitoring

The zebra mussel (Dreissena polymorpha) represents a useful reference organism for the ecotoxicological study of inland waters, especially for the characterization of the disturbances induced by human activities. A nuclear magnetic resonance (NMR)-based metabolomic approach was developed on this species. The investigation of its informative potential required the prior interpretation of a reference ¹H NMR spectrum of a lipid-free zebra mussel extract. After the extraction of polar metabolites from a pool of whole-body D. polymorpha powder, the resulting highly complex 1D ¹H NMR spectrum was interpreted and annotated through the analysis of the corresponding 2D homonuclear and heteronuclear NMR spectra. The spectrum interpretation was completed and validated by means of sample spiking with 24 commercial compounds. Among the 238 detected ¹H signals, 53% were assigned, resulting in the identification of 37 metabolites with certainty or high confidence, while 5 metabolites were only putatively identified. The description of such a reference spectrum and its annotation are expected to speed up future analyses and interpretations of NMR-based metabolomic studies on D. polymorpha and to facilitate further explorations of the impact of environmental changes on its physiological state, more particularly in the context of large-scale ecological and ecotoxicological studies.

Metabolic Variations between Low-Grade and High-Grade Gliomas-Profiling by 1H NMR Spectroscopy

Altered cellular metabolism is one of the crucial hallmarks of glioma that deserves exploration, as the metabolites act as direct indicators of protein function and genetic variations. The current study focused on the metabolomic profiling of patients from whom glioma specimens were obtained for the identification of specific metabolites that could distinguish the low grade and high grade. In the current study, ¹H NMR spectroscopy was carried out and the data were analyzed by partial least-squares discriminant analysis (PLS-DA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). Pathway analysis was done to associate characteristic metabolites with the grades of sample using MetaboAnalyst 4.0 software based on the KEGG metabolic pathways database. Distinctive metabolic profiles among low- and high-grade gliomas with top 15 characteristic metabolites that could discriminate these grades were identified on the basis of their VIP scores from the OPLS-DA model. The major altered metabolic pathways include choline, taurine and hypotaurine, glutamate/glutamine, glutathione, and phenyl alanine/tyrosine, which were found to be consistent with the particular grade of a sample. Our study clearly demonstrated a characteristic metabolic profile of individual grades of glioma, suggesting that an altered metabolism is consistent with the specific grades of glioma appreciation and could lead to the development novel treatment strategies.

1H NMR-Based Urine Metabolomics Reveals Signs of Enhanced Carbon and Nitrogen Recycling in Prostate Cancer

Prostate cancer is the second most common tumor and the fifth cause of cancer-related death among men worldwide. PC cells exhibit profound signaling and metabolic reprogramming that account for the acquisition of aggressive features. Although the metabolic understanding of this disease has increased in recent years, the analysis of such alterations through noninvasive methodologies in biofluids remains limited. Here, we used NMR-based metabolomics on a large cohort of urine samples (more than 650) from PC and benign prostate hyperplasia (BPH) patients to investigate the molecular basis of this disease. Multivariate analysis failed to distinguish between the two classes, highlighting the modest impact of prostate alterations on urine composition and the multifactorial nature of PC. However, univariate analysis of urine metabolites unveiled significant changes, discriminating PC from BPH. Metabolites with altered abundance in urine from PC patients revealed changes in pathways related to cancer biology, including glycolysis and the urea cycle. We found out that metabolites from such pathways were diminished in the urine from PC individuals, strongly supporting the notion that PC reduces nitrogen and carbon waste in order to maximize their usage in anabolic processes that support cancer cell growth.

Proton Nuclear Magnetic Resonance Metabolomics Corroborates Serine Hydroxymethyltransferase as the Primary Target of 2-Aminoacrylate in a ridA Mutant of Salmonella enterica

The accumulation of the reactive enamine intermediate 2-aminoacrylate (2AA) elicits global metabolic stress in many prokaryotes and eukaryotes by simultaneously damaging multiple pyridoxal 5′-phosphate (PLP)-dependent enzymes. This work employed ¹H NMR to expand our understanding of the consequence(s) of 2AA stress on metabolite pools and effectively identify the metabolic changes stemming from one damaged target: GlyA. This study shows that nutrient supplementation during ¹H NMR metabolomics experiments can disentangle complex metabolic outcomes stemming from a general metabolic stress. Metabolomics shows great potential to complement classical reductionist approaches to cost-effectively accelerate the rate of progress in expanding our global understanding of metabolic network structure and physiology. To that end, this work demonstrates the utility in implementing nutrient supplementation and genetic perturbation into metabolomics workflows as a means to connect metabolic outputs to physiological phenomena and establish causal relationships.

The reactive intermediate deaminase RidA (EC 3.5.99.10) is conserved across all domains of life and deaminates reactive enamine species. When Salmonella entericaridA mutants are grown in minimal medium, 2-aminoacrylate (2AA) accumulates, damages several pyridoxal 5′-phosphate (PLP)-dependent enzymes, and elicits an observable growth defect. Genetic studies suggested that damage to serine hydroxymethyltransferase (GlyA), and the resultant depletion of 5,10-methelenetetrahydrofolate (5,10-mTHF), was responsible for the observed growth defect. However, the downstream metabolic consequence from GlyA damage by 2AA remains relatively unexplored. This study sought to use untargeted proton nuclear magnetic resonance (¹H NMR) metabolomics to determine whether the metabolic state of an S. entericaridA mutant was accurately reflected by characterizing growth phenotypes. The data supported the conclusion that metabolic changes in a ridA mutant were due to the IlvA-dependent generation of 2AA, and that the majority of these changes were a consequence of damage to GlyA. While many of the metabolic differences for a ridA mutant could be explained, changes in some metabolites were not easily modeled, suggesting that additional levels of metabolic complexity remain to be unraveled.

IMPORTANCE The accumulation of the reactive enamine intermediate 2-aminoacrylate (2AA) elicits global metabolic stress in many prokaryotes and eukaryotes by simultaneously damaging multiple pyridoxal 5′-phosphate (PLP)-dependent enzymes. This work employed ¹H NMR to expand our understanding of the consequence(s) of 2AA stress on metabolite pools and effectively identify the metabolic changes stemming from one damaged target: GlyA. This study shows that nutrient supplementation during ¹H NMR metabolomics experiments can disentangle complex metabolic outcomes stemming from a general metabolic stress. Metabolomics shows great potential to complement classical reductionist approaches to cost-effectively accelerate the rate of progress in expanding our global understanding of metabolic network structure and physiology. To that end, this work demonstrates the utility in implementing nutrient supplementation and genetic perturbation into metabolomics workflows as a means to connect metabolic outputs to physiological phenomena and establish causal relationships.

High-resolution 1H-NMR spectroscopy indicates variations in metabolomics profile of follicular fluid from women with advanced maternal age

AIM

To reveal whether there are differences in follicular fluid metabolomics profile of women with advanced maternal age (AMA).

METHOD

The group with advanced maternal age includes 23 patients above the age of 40, and the control group includes 31 patients aged between 25 and 35 years and AMH values above 1.1 ng/mL with no low ovarian response history. A single follicular fluid sample from a MII oocyte obtained during the oocyte pick-up procedure was analyzed with high-resolution ¹H-NMR (nuclear magnetic resonance) spectroscopy. The results were evaluated using advanced bioinformatics analysis methods.

RESULTS

Statistical analysis of the NMR spectroscopy data from two groups showed that α-glucose and β-glucose levels of follicular fluid were decreased in the patients with AMA, while in contrast, lactate and trimethylamine N-oxide (TMAO) levels were increased in these patients compared with the controls. In addition to these, there was an increase in alanine levels and a decrease in acetoacetate levels in patients with AMA. However, these changes were not statistically significant.

CONCLUSION

Obtained results suggest that the follicular cell metabolism of patients with AMA is different from controls. These environmental changes could be associated with the low success rates of IVF treatment seen in these patients.

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

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

NMR-based metabolomics and fluxomics: developments and future prospects

Recent NMR developments are acting as game changers for metabolomics and fluxomics – a critical and perspective review.

Identity and functions of inorganic and inositol polyphosphates in plants

Inorganic polyphosphates (polyPs) and inositol pyrophosphates (PP-InsPs) form important stores of inorganic phosphate and can act as energy metabolites and signaling molecules. Here we review our current understanding of polyP and inositol phosphate (InsP) metabolism and physiology in plants. We outline methods for polyP and InsP detection, discuss the known plant enzymes involved in their synthesis and breakdown, and summarize the potential physiological and signaling functions for these enigmatic molecules in plants.

TDCIPP exposure affects Artemia franciscana growth and osmoregulation

Environmental monitoring has demonstrated widespread occurrence of the flame-retardant tris(1,3-dichloro-2-propyl) phosphate (TDCIPP), raising concerns about the impact on aquatic life. Using ¹H NMR and GC-MS metabolomics and 20-day body length experiments, we have determined that exposure to TDCIPP affects Artemia franciscana. The LC₅₀ for a 48 h TDCIPP exposure was determined to be 37.1 ± 1.3 μM. Acute exposure (48 h) to 20.0 μM did not affect A. franciscana body length but did elicit a metabolic change. Chronic exposure to 0.50 μM TDCIPP caused decreased body length in A. franciscana exposed for 20 days and elicited a metabolic response. Principal component analysis revealed variance between acute and chronic exposure along PC1 (36.4%) and between control and TDCIPP along PC2 (17.4%). One-way ANOVA indicated that 19 metabolites were significantly affected by TDCIPP exposure; namely metabolites of the osmolyte class, including betaine, phosphocholine, gadusol, taurine, glycerol and trehalose - metabolites that are essential osmoprotectants in extremophile species. Other pathways that may be perturbed by TDCIPP exposure include one carbon, glycine, serine, threonine, and glycerophospholipid metabolism.

Differences in the composition of hip and knee synovial fluid in osteoarthritis: a nuclear magnetic resonance (NMR) spectroscopy study of metabolic profiles

OBJECTIVE

The hip and knee joints differ biomechanically in terms of contact stresses, fluid lubrication and wear patterns. These differences may be reflected in the synovial fluid (SF) composition of the two joints, but the nature of these differences remains unknown. The objective was to identify differences in osteoarthritic hip and knee SF metabolites using metabolic profiling with Nuclear Magnetic Resonance (NMR) spectroscopy.

DESIGN

Twenty-four SF samples (12 hip, 12 knee) were collected from patients with end-stage osteoarthritis (ESOA) undergoing hip/knee arthroplasty. Samples were matched for age, gender, ethnicity and had similar medical comorbidities. NMR spectroscopy was used to analyse the metabolites present in each sample. Principal Component Analysis and Orthogonal Partial Least Squares Discriminant Analysis were undertaken to investigate metabolic differences between the groups. Metabolites were identified using 2D NMR spectra, statistical spectroscopy and by comparison to entries in published databases.

RESULTS

There were significant differences in the metabolic profile between the groups. Four metabolites were found in significantly greater quantities in the knee group compared to the hip group (N-acetylated molecules, glycosaminoglycans, citrate and glutamine).

CONCLUSIONS

This is the first study to indicate differences in the metabolic profile of hip and knee SF in ESOA. The identified metabolites can broadly be grouped into those involved in collagen degradation, the tricarboxylic acid cycle and oxidative metabolism in diseased joints. These findings may represent a combination of intra and extra-articular factors.

NMR-based newborn urine screening for optimized detection of inherited errors of metabolism

Inborn errors of metabolism (IEMs) are rare diseases produced by the accumulation of abnormal amounts of metabolites, toxic to the newborn. When not detected on time, they can lead to irreversible physiological and psychological sequels or even demise. Metabolomics has emerged as an efficient and powerful tool for IEM detection in newborns, children, and adults with late onset. In here, we screened urine samples from a large set of neonates (470 individuals) from a homogeneous population (Basque Country), for the identification of congenital metabolic diseases using NMR spectroscopy. Absolute quantification allowed to derive a probability function for up to 66 metabolites that adequately describes their normal concentration ranges in newborns from the Basque Country. The absence of another 84 metabolites, considered abnormal, was routinely verified in the healthy newborn population and confirmed for all but 2 samples, of which one showed toxic concentrations of metabolites associated to ketosis and the other one a high trimethylamine concentration that strongly suggested an episode of trimethylaminuria. Thus, a non-invasive and readily accessible urine sample contains enough information to assess the potential existence of a substantial number (>70) of IEMs in newborns, using a single, automated and standardized ¹H- NMR-based analysis.

Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome

The gut microbiome has been shown to play a significant role in human healthy and diseased states. The dynamic signaling that occurs between the host and microbiome is critical for the maintenance of host homeostasis. Analyzing the human microbiome with metaproteomics, metabolomics, and integrative multi-omics analyses can provide significant information on markers for healthy and diseased states, allowing for the eventual creation of microbiome-targeted treatments for diseases associated with dysbiosis. Metaproteomics enables functional activity information to be gained from the microbiome samples, while metabolomics provides insight into the overall metabolic states affecting/representing the host-microbiome interactions. Combining these functional -omic platforms together with microbiome composition profiling allows for a holistic overview on the functional and metabolic state of the microbiome and its influence on human health. Here the benefits of metaproteomics, metabolomics, and the integrative multi-omic approaches to investigating the gut microbiome in the context of human health and diseases are reviewed.

Investigating the intracellular effects of hyperbranched polycation-DNA complexes on lung cancer cells using LC-MS-based metabolite profiling

Cationic polymers have emerged as a promising alternative to viral vectors in gene therapy. They are cheap to scale up, easy to functionalise and are potentially safer than viral vectors, however many are cytotoxic. The large number of polycations, designed to address the toxicity problem, raises a practical need to develop a fast and reliable method for assessing the safety of these materials. In this regard, metabolomics provides a detailed and comprehensive method that can assess the potential toxicity at the cellular and molecular level. Here, we applied metabolomics to investigate the impact of hyperbranched polylysine, hyperbranched polylysine-co-histidine and branched polyethyleneimine polyplexes at sub-toxic concentrations on the metabolic pathways of A459 and H1299 lung carcinoma cell lines. The study revealed that the polyplexes downregulated metabolites associated with glycolysis and the TCA cycle, and induced oxidative stress in both cell lines. The relative changes of the metabolites indicated that the polyplexes of polyethyleneimine and hyperbranched polylysine affected the metabolism much more than the polyplexes of hyperbranched polylysine-co-histidine. This was in line with transfection results, suggesting a correlation between the toxicity and transfection efficiency of these polyplexes. Our work highlights the importance of the metabolomics approach not just to assess the potential toxicity of polyplexes but also to understand the molecular mechanisms underlying any adverse effects, which could help in designing more efficient vectors.

Antibiotic Discovery: Where Have We Come from, Where Do We Go?

Given the increase in antibiotic-resistant bacteria, alongside the alarmingly low rate of newly approved antibiotics for clinical usage, we are on the verge of not having effective treatments for many common infectious diseases. Historically, antibiotic discovery has been crucial in outpacing resistance and success is closely related to systematic procedures—platforms—that have catalyzed the antibiotic golden age, namely the Waksman platform, followed by the platforms of semi-synthesis and fully synthetic antibiotics. Said platforms resulted in the major antibiotic classes: aminoglycosides, amphenicols, ansamycins, beta-lactams, lipopeptides, diaminopyrimidines, fosfomycins, imidazoles, macrolides, oxazolidinones, streptogramins, polymyxins, sulphonamides, glycopeptides, quinolones and tetracyclines. During the genomics era came the target-based platform, mostly considered a failure due to limitations in translating drugs to the clinic. Therefore, cell-based platforms were re-instituted, and are still of the utmost importance in the fight against infectious diseases. Although the antibiotic pipeline is still lackluster, especially of new classes and novel mechanisms of action, in the post-genomic era, there is an increasingly large set of information available on microbial metabolism. The translation of such knowledge into novel platforms will hopefully result in the discovery of new and better therapeutics, which can sway the war on infectious diseases back in our favor.

Metabolomics in chronic lung diseases

Chronic lung diseases represent a significant global burden. Their increasing incidence and complexity render a comprehensive, multidisciplinary and personalized approach to each patient, critically important. Most recently, unique biochemical pathways and disease markers have been identified through large-scale metabolomic studies. Metabolomics is the study of metabolic pathways and the measurement of unique biomolecules in a living system. Analysing samples from different compartments such as bronchoalveolar lavage fluid (BALF) and plasma has proven useful for the characterization of a number of pathological conditions and offers promise as a clinical tool. For example, several studies using mass spectrometry (MS) have shown alterations in the sphingolipid metabolism of chronic obstructive pulmonary disease (COPD) sufferers. In this article, we present a practical review of the application of metabolomics to the study of chronic lung diseases (CLD): COPD, idiopathic pulmonary fibrosis (IPF) and asthma. The insights, which the analytical strategies employed in metabolomics, have provided to the dissection of the biochemistry of CLD and future clinical biomarkers are explored.

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.

Identification of antiplasmodial triterpenes from Keetia species using NMR-based metabolic profiling

INTRODUCTION

The increase in multidrug resistance and lack of efficacy in malaria therapy has propelled the urgent discovery of new antiplasmodial drugs, reviving the screening of secondary metabolites from traditional medicine. In plant metabolomics, NMR-based strategies are considered a golden method providing both a holistic view of the chemical profiles and a correlation between the metabolome and bioactivity, becoming a corner stone of drug development from natural products.

OBJECTIVE

Create a multivariate model to identify antiplasmodial metabolites from ¹H NMR data of two African medicinal plants, Keetia leucantha and K. venosa.

METHODS

The extracts of twigs and leaves of Keetia species were measured by ¹H NMR and the spectra were submitted to orthogonal partial least squares (OPLS) for antiplasmodial correlation.

RESULTS

Unsupervised ¹H NMR analysis showed that the effect of tissues was higher than species and that triterpenoids signals were more associated to Keetia twigs than leaves. OPLS-DA based on Keetia species correlated triterpene signals to K. leucantha, exhibiting a higher concentration of triterpenoids and phenylpropanoid-conjugated triterpenes than K. venosa. In vitro antiplasmodial correlation by OPLS, validated for all Keetia samples, revealed that phenylpropanoid-conjugated triterpenes were highly correlated to the bioactivity, while the acyclic squalene was found as the major metabolite in low bioactivity samples.

CONCLUSION

NMR-based metabolomics combined with supervised multivariate data analysis is a powerful strategy for the identification of bioactive metabolites in plant extracts. Moreover, combination of statistical total correlation spectroscopy with 2D NMR allowed a detailed analysis of different triterpenes, overcoming the challenge posed by their structure similarity and coalescence in the aliphatic region.

Sex differences in the 1H NMR metabolic profile of serum in cardiovascular risk patients

Personalized diagnosis and risk stratification of cardiovascular diseases would allow optimizing therapeutic strategies and lifestyle changes. Metabolomics is a promising technique for personalized diagnosis and prognosis; however, various physiological parameters, including sex, influence the metabolic profile thus hampering its translation to the clinic. Knowledge of the variation in the metabolic profile associated with sex would facilitate metabolomic translation to the clinic. The objective of the present work was to investigate the possible differences in the metabolic ¹H NMR profile associated to sex beyond lipoproteins. ¹H NMR spectra from whole serum and methanol deproteinized samples from 39 patients (22 males, 17 females) between 55–70 years old with suspected coronary artery disease that underwent a stress test that was considered negative where included. Deproteinized serum could be used to differentiate sex based on higher levels of lactate and glucose in women. Lipoprotein region was the most variable area of the spectra between individuals, but spectra of whole serum were able to differentiate sex based on lipoproteins. There are sex-related differences in the ¹H NMR metabolic profile of individuals with suspected cardiovascular disease beyond lipoproteins. These findings may help the translation of metabolomics to the clinic.

HR-MAS NMR Based Quantitative Metabolomics in Breast Cancer

High resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy is increasingly used for profiling of breast cancer tissue, delivering quantitative information for approximately 40 metabolites. One unique advantage of the method is that it can be used to analyse intact tissue, thereby requiring only minimal sample preparation. Importantly, since the method is non-destructive, it allows further investigations of the same specimen using for instance transcriptomics. Here, we discuss technical aspects critical for a successful analysis—including sample handling, measurement conditions, pulse sequences for one- and two dimensional analysis, and quantification methods—and summarize available studies, with a focus on significant associations of metabolite levels with clinically relevant parameters.

Extending the Scope of 1H NMR Spectroscopy for the Analysis of Cellular Coenzyme A and Acetyl Coenzyme A

Coenzyme A (CoA) and acetyl-coenzyme A (acetyl-CoA) are ubiquitous cellular molecules, which mediate hundreds of anabolic and catabolic reactions including energy metabolism. Highly sensitive methods including absorption spectroscopy and mass spectrometry enable their analysis, albeit with many limitations. To date, however, NMR spectroscopy has not been used to analyze these important molecules. Building on our recent efforts, which enabled simultaneous analysis of a large number of metabolites in tissue and blood including many coenzymes and antioxidants ( Anal. Chem. 2016, 88, 4817-24; ibid 2017, 89, 4620-4627), we describe here a new method for identification and quantitation of CoA and acetyl-CoA ex vivo in tissue. Using mouse heart, kidney, liver, brain, and skeletal tissue, we show that a simple ¹H NMR experiment can simultaneously measure these molecules. Identification of the two species involved a comprehensive analysis of the different tissue types using 1D and 2D NMR, in combination with spectral databases for standards, as well as spiking with authentic compounds. Time dependent studies showed that while the acetyl-CoA levels remain unaltered, CoA levels diminish by more than 50% within 24 h, which indicates that CoA is labile in solution; however, degassing the sample with helium gas halted its oxidation. Further, interestingly, we also identified endogenous coenzyme A glutathione disulfide (CoA-S-S-G) in tissue for the first time by NMR and show that CoA, when oxidized in tissue extract, also forms the same disulfide metabolite. The ability to simultaneously visualize absolute concentrations of CoA, acetyl-CoA, and endogenous CoA-S-S-G along with redox coenzymes (NAD⁺, NADH, NADP⁺, NADPH), energy coenzymes (ATP, ADP, AMP), antioxidants (GSH, GSSG), and a vast pool of other metabolites using a single 1D NMR spectrum offers a new avenue in the metabolomics field for investigation of cellular function in health and disease.

Novel strategies for clinical investigation and biomarker discovery: a guide to applied metabolomics

Metabolomics is an emerging technology that is increasing both in basic science and in human applications, providing a physiological snapshot. It has been highlighted as one of the most wide ranging and reliable tools for the investigation of physiological status, the discovery of new biomarkers and the analysis of metabolic pathways. Metabolomics uses innovative mass spectrometry (MS) allied to chromatography or nuclear magnetic resonance (NMR). The recent advances in bioinformatics, databases and statistics, have provided a unique perception of metabolites interaction and the dynamics of metabolic pathways at a system level. In this context, several studies have applied metabolomics in physiology- and disease-related works. The application of metabolomics includes, physiological and metabolic evaluation/monitoring, individual response to different exercise, nutritional interventions, pathological processes, responses to pharmacological interventions, biomarker discovery and monitoring for distinct aspects, such as: physiological capacity, fatigue/recovery and aging among other applications. For metabolomic analyses, despite huge improvements in the field, several complex methodological steps must be taken into consideration. In this regard, the present article aims to summarize the novel aspects of metabolomics and provide a guide for metabolomics for professionals related to physiologist and medical applications.

A scoring approach for multi-platform acquisition in metabolomics

Since the ultimate goal of untargeted metabolomics is the analysis of the broadest possible range of metabolites, some new metrics have to be used by researchers to evaluate and select different analytical strategies when multi-platform analyses are considered. In this context, we aimed at developing a scoring approach allowing to compare the performance of different LC-MS conditions for metabolomics studies. By taking into account both chromatographic and MS attributes of the analytes' peaks (i.e. retention, signal-to-noise ratio, peak intensity and shape), the newly proposed score reflects the potential of a set of LC-MS operating conditions to provide useful analytical information for a given compound. A chemical library containing 597 metabolites was used as a benchmark to apply this approach on two RPLC and three HILIC methods hyphenated to high resolution mass spectrometry (HRMS) in positive and negative ionization modes. The scores not only allowed to evaluate each analytical platform, but also to optimize the number of analytical methods needed for the analysis of metabolomics samples. As a result, the most informative combination of three LC methods and ionization modes was found, leading to a coverage of nearly 95% of the detected compounds. It was therefore demonstrated that the overall performance reached with three selected methods was almost equivalent to the performance reached when five LC-MS conditions were used.

Effects of obesity on the lipid and metabolite profiles of young adults by serum 1H-NMR spectroscopy

BACKGROUND

Overweight (OW) is considered a risk for various metabolic diseases. However, its effects as a mechanism that alters the metabolite profiles remain unclear. The purpose of this study is to investigate the effects that OW has on the lipid and metabolite profiles in young adults.

METHODS

The serum metabolite profiles of 46 young adults of normal weight and those considered OW were studied by Proton nuclear magnetic resonance spectroscopy (¹H NMR) technique.

RESULTS

¹H NMR metabolite analysis shows the alteration of metabolic levels and increased levels of CH₂ lipids and CH₃ lipids, which are used as unique biomarkers to identify OW subjects from the normal weight groups.

CONCLUSION

This present study reveals that OW contributes to the systemic metabolism and the metabolite alteration among young adults. The alteration in serum lipids level could shed the light on metabolic syndrome pathogenesis in young adults and needs further elucidation.

Overview of NMR Spectroscopy-Based Metabolomics: Opportunities and Challenges

The fast-growing field of metabolomics is impacting numerous areas of basic and life sciences. In metabolomics, analytical methods play a pivotal role, and nuclear magnetic resonance (NMR) and mass spectrometry (MS) have proven to be the most suitable and powerful methods. Although NMR exhibits lower sensitivity and resolution compared to MS, NMR's numerous important characteristics far outweigh its limitations. Some of its characteristics include excellent reproducibility and quantitative accuracy, the capability to analyze intact biospecimens, an unparalleled ability to identify unknown metabolites, the ability to trace in-cell and in-organelle metabolism in real time, and the capacity to trace metabolic pathways atom by atom using ²H, ¹³C, or ¹⁵N isotopes. Each of these characteristics has been exploited extensively in numerous studies. In parallel, the field has witnessed significant progress in instrumentation, methods development, databases, and automation that are focused on higher throughput and alleviating the limitations of NMR, in particular, resolution and sensitivity. Despite the advances, however, the high complexity of biological mixtures combined with the limitations in sensitivity and resolution continues to pose major challenges. These challenges need to be dealt with effectively to better realize the potential of metabolomics, in general. As a result, multifaceted efforts continue to focus on addressing the challenges as well as reaping the benefits of NMR-based metabolomics. This chapter highlights the current status with emphasis on the opportunities and challenges in NMR-based metabolomics.

Network analysis of membranous glomerulonephritis based on metabolomics data

Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component‑reaction‑enzyme‑gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography‑mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold‑change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein‑protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold‑changes: Dopamine, carnosine, fumarate, nicotinamide D‑ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L‑citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine‑4‑hydroxlylase and cystathionine γ‑lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: 'Pyrimidine‑containing compound biosynthetic process', 'purine ribonucleoside metabolic process', 'nucleoside catabolic process', 'ribonucleoside metabolic process' and 'aromatic amino acid family metabolic process'. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.

Monitoring daily routine performance in quantitative NMR (qNMR) spectroscopy: Is the system suitability test necessary?

Nuclear magnetic resonance spectrometry (NMR) finds numerous applications in pharmacy, cosmetic, and food control as well as in developing tools for "big data" analysis. However, there remains a need for automated tools to assess instrument system suitability in real time for each particular routine sample. An automated procedure has been introduced to monitor a number of characteristics (resolution, symmetry, and half width) in real time after the measurement of two samples distributed by the vendor (0.3% CHCl3 in acetone-d6 with tetramethylsilane and 2 mM sucrose in H2 O-D2 O). The results over 11 months were discussed in terms of average values, standard deviations, and spectrometer variability. Moreover, multivariate statistical procedure was implemented to evaluate metrics generated from three NMR spectrometers. Performance of three NMR spectrometers (500 MHz with BBO Prodigy Cryoprobe, 500 MHz with BBFOPLUS SmartProbe, and 600 MHz with BBO Cryoprobe) differed significantly. The developed routine was also applied to calculate the performance characteristics during routine quantitative NMR experiments. The procedure was evaluated for NMR spectra of 659 active pharmaceutical ingredients dissolved in CDCl3 , DMSO, and CH3 OD. This test is more preferable than the routine procedure using standard solutions because the performance is estimated separately for each matrix at the specific time point of measurements. Our automated routine is the ideal tool for any NMR laboratory. In full automation, the NMR data are validated directly for each sample, making unnecessary daily measurements of standard solutions and manual evaluation to their NMR spectra.

NMR detection in biofluid extracts at sub-μM concentrations via para-H2 induced hyperpolarization

NMR spectroscopy is one of the most powerful techniques to simultaneously obtain qualitative and quantitative information in chemical analysis. Despite its versatility, the applications of NMR in the study of biofluids are often limited by the insensitivity of the technique, further aggravated by the poor signal dispersion in the (1)H spectra. Recent advances in para-H2 induced hyperpolarization have proven to address both these limitations for specific classes of compounds. Herein, this approach is for the first time applied for quantitative determination in biofluid extracts. We demonstrate that a combination of solid phase extraction, para-hydrogen induced hyperpolarization and selective NMR detection quickly reveals a doping substance, nikethamide, at sub-μM concentrations in urine. We suggest that this method can be further optimized for the detection of different analytes in various biofluids, anticipating a wider application of hyperpolarized NMR in metabolomics and pharmacokinetics studies in the near future.

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

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

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide and the fifth leading cause of death from cancer in men. Early detection and risk stratification is the most effective way to improve the survival of PCa patients. Current PCa biomarkers lack sufficient sensitivity and specificity to cancer. Metabolite biomarkers are evolving as a new diagnostic tool. This review is aimed to evaluate the potential of metabolite biomarkers for early detection, risk assessment, and monitoring of PCa. Of the 154 identified publications, 27 and 38 were original papers on urine and serum metabolomics, respectively. Nuclear magnetic resonance (NMR) is a promising method for measuring concentrations of metabolites in complex samples with good reproducibility, high sensitivity, and simple sample processing. Especially urine-based NMR metabolomics has the potential to be a cost-efficient method for the early detection of PCa, risk stratification, and monitoring treatment efficacy.

An Overview of Metabolic Phenotyping in Blood Pressure Research

PURPOSE OF THE REVIEW

This review presents the analytical techniques, processing and analytical steps used in metabolomics phenotyping studies, as well as the main results from epidemiological studies on the associations between metabolites and high blood pressure.

RECENT FINDINGS

A variety of metabolomic approaches have been applied to a range of epidemiological studies to uncover the pathophysiology of high blood pressure. Several pathways have been suggested in relation to blood pressure including the possible role of the gut microflora, inflammatory, oxidative stress, and lipid pathways. Metabolic changes have also been identified associated with blood pressure lowering effects of diets high in fruits and vegetables and low in meat intake. However, the current body of literature on metabolic profiling and blood pressure is still in its infancy, not fully consistent and requires careful interpretation. Metabolic phenotyping is a promising approach to uncover metabolic pathways associated with high blood pressure and throw light into the complex pathophysiology of hypertension.

Transformed Root Culture: From Genetic Transformation to NMR-Based Metabolomics

Hairy root (HR) culture is considered as "green factory" for mass production of bioactive molecules with pharmaceutical relevance. As such, HR culture has an immense potential as a valuable platform to elucidate biosynthetic pathways and physiological processes, generate recombinant therapeutic proteins, assist molecular breeding, and enhance phytoremediation efforts. However, some plant species appear recalcitrant to the classical Agrobacterium rhizogenes transformation techniques. Sonication-assisted Agrobacterium-mediated transformation (SAArT) is a highly effective method to deliver bacteria to target plant tissues that includes exposure of the explants to short periods of ultrasound in the presence of the bacteria.Nuclear magnetic resonance (NMR)-based metabolomics is one of the most powerful and suitable platforms for identifying and obtaining structural information on a wide range of compounds with a high analytical precision. In terms of plant science, NMR metabolomics is used to determine the phytochemical variations of medicinal plants or commercial cultivars in certain environments and conditions, including biotic stress and plant biotic interaction, structural determination of natural products, quality control of herbal drugs or dietary supplements, and comparison of metabolite differences between plants and their respective in vitro cultures.In this chapter, we attempt to summarize our knowledge and expertise in induction of hairy roots from rare and recalcitrant plant species by SAArT technique and further methodology for extraction of secondary metabolites of moderate to high polarity and their identification by using NMR-based metabolomics.

Profiling Redox and Energy Coenzymes in Whole Blood, Tissue and Cells Using NMR Spectroscopy

Coenzymes of cellular redox reactions and cellular energy, as well as antioxidants mediate biochemical reactions fundamental to the functioning of all living cells. Conventional analysis methods lack the opportunity to evaluate these important redox and energy coenzymes and antioxidants in a single step. Major coenzymes include redox coenzymes: NAD⁺ (oxidized nicotinamide adenine dinucleotide), NADH (reduced nicotinamide adenine dinucleotide), NADP⁺ (oxidized nicotinamide adenine dinucleotide phosphate) and NADPH (reduced nicotinamide adenine dinucleotide phosphate); energy coenzymes: ATP (adenosine triphosphate), ADP (adenosine diphosphate) and AMP (adenosine monophosphate); and antioxidants: GSSG (oxidized glutathione) and GSH (reduced glutathione). We show here that a simple ¹H NMR experiment can measure these coenzymes and antioxidants in tissue and whole blood apart from a vast pool of other metabolites. In addition, focused on the goal of identification of coenzymes in subcellular fractions, we demonstrate analysis of coenzymes in the cytoplasm using breast cancer cells. Owing to their unstable nature, or low concentrations, most of the coenzymes either evade detection or lose their integrity when established sample preparation and analysis methods are used. To overcome this challenge, here we describe the development of new methods to detect these molecules without affecting the integrity of other metabolites. We used an array of 1D and 2D NMR methods, chemical shift databases, pH measurements and spiking with authentic compounds to establish the identity of peaks for the coenzymes and antioxidants in NMR spectra. Interestingly, while none of the coenzymes and antioxidants were detected in plasma, they were abundant in whole blood. Considering that the coenzymes and antioxidants represent a sensitive measure of human health and risk for numerous diseases, the presented NMR methods to measure them in one step potentially open new opportunities in the metabolomics field.

Characterization of bioactive compounds of Annona cherimola L. leaves using a combined approach based on HPLC-ESI-TOF-MS and NMR

Annona cherimola Mill. (cherimoya) has widely been used as food crop. The leaves of this tree possess several health benefits, which are, in general, attributed mainly to its bioactive composition. However, literature concerning a comprehensive characterization based on a combined approach, which consists of nuclear magnetic resonance (NMR) and high-performance liquid chromatography coupled with time-of-flight mass spectrometry (HPLC-TOF-MS), from these leaves is scarce. Thus, the aim of this work was to study the polar profile of full extracts of cherimoya leaves by using these tools. Thus, a total of 77 compounds have been characterized, 12 of which were identified by both techniques. Briefly, 23 compounds were classified as amino acids, organic acids, carbohydrates, cholines, phenolic acid derivatives, and flavonoids by NMR, while 66 metabolites were divided into sugars, amino acids, phenolic acids and derivatives, flavonoids, phenylpropanoids, and other polar compounds by HPLC-TOF-MS. It is worth mentioning that different solvent mixtures were tested and the total phenolic content in the extracts quantified (TPC via HPLC-TOF-MS). The tendency observed was EtOH/water 80/20 (v/v) (17.0 ± 0.2 mg TPC/g leaf dry weight (d.w.)) ≥ acetone/water 70/30 (v/v) (16.1 ± 0.7 mg TPC/g leaf d.w.) > EtOH/water 70/30 (v/v) (14.0 ± 0.3 mg TPC/g leaf d.w.) > acetone/water 80/20 (v/v) (13.5 ± 0.4 mg TPC/g leaf d.w.). Importantly, flavonoids derivatives were between 63 and 76% of the TPC in those extracts. Major compounds were sucrose, glucose (α and β), and proline, and chlorogenic acid and rutin for NMR and HPLC-TOF-MS, respectively. Graphical abstract The combined use of LC-HRMS and NMR is a potential synergic combination for a comprehensive metabolite composition of cherimoya leaves.