1D 1H Nuclear Magnetic Resonance Research Articles

Automatic Chemical Profiling of Wine by Proton Nuclear Magnetic Resonance Spectroscopy.

We report the development of MagMet-W (magnetic resonance for metabolomics of wine), a software program that can automatically determine the chemical composition of wine via 1H nuclear magnetic resonance (NMR) spectroscopy. MagMet-W is an extension of MagMet developed for the automated metabolomic analysis of human serum by 1H NMR. We identified 70 compounds suitable for inclusion into MagMet-W. We then obtained 1D 1H NMR reference spectra of the pure compounds at 700 MHz and incorporated these spectra into the MagMet-W compound library. The processing of the wine NMR spectra and profiling of the 70 wine compounds were then optimized based on manual 1H NMR analysis. MagMet-W can automatically identify 70 wine compounds in most wine samples and can quantify them to 10-15% of the manually determined concentrations, and it can analyze multiple spectra simultaneously, at 10 min per spectrum. The MagMet-W Web server is available at https://www.magmet.ca.

Intact NMR Approach Quickly Reveals Synchronized Microstructural Changes in Oil-in-Water Nanoemulsion Formulations

A soft-core oil-in-water (o/w) nanoemulsion (NE) is composed of nanometer (nm) sized oil droplets, stabilized by a surfactant layer and dispersed in a continuous bulky water phase. Characterization of the o/w NE molecule arrangements non-invasively, particularly the drug phase distribution (DPD) and its correlation to oil globule size (OGS), remains a challenge. Here we demonstrated the analytical methods of intact 19F Nuclear Magnetic Resonance (NMR) and 1H diffusion ordered spectroscopy (DOSY) NMR for their specificity in measuring DPD and OGS, respectively, on three NE formulations containing the active ingredient difluprednate (DFPN) at the same concentration. The results illustrated synchronized molecular rearrangement reflected in the DPD and OGS upon alterations in formulation. Addition of surfactant resulted in a higher DPD in the surfactant layer, and concomitantly smaller OGS. Mechanic perturbation converted most of the NE globules to the smaller thermodynamically stable microemulsion (ME) globules, changing both DPD and OGS to ME phase. These microstructure changes were not observed using 1D 1H NMR; and dynamic light scattering (DLS) was only sensitive to OGS of ME globule in mechanically perturbed formulation. Collectively, the study illustrated the specificity and essential role of intact NMR methods in measuring the critical microstructure attributes of soft-core NE systems quickly, accurately, and non-invasively. Therefore, the selected NMR approach can be a unique diagnostic tool of molecular microstructure or Q3 property in o/w NE formulation development, and quality assurance after manufacture process or excipient component changes.Graphical abstract

Characterization of a Simulated Martian Regolith Ecosystem by Proton Nuclear Magnetic Resonance (NMR) Relaxometry and Fourier Transform Infrared (FT-IR) and Visible (VIS)-Near-Infrared (NIR) Spectroscopies

Of the various Martian soil simulant mixes developed by NASA and Jet Propulsion Laboratory (JPL), three types were chosen that have the closest features to those of the Martian regolith to be analyzed. The characterization of the Martian regolith types was performed the advanced methods of 1H nuclear magnetic resonance (NMR) relaxometry, Fourier transform infrared (FT-IR) spectroscopy and visible (VIS)-near-infrared (NIR) spectroscopy, as well as classical methods such as pH, the electrical conductivity, and the total dissolved solids. These analyses showed that trace water, nitrogen, phosphorus and potassium are present in the Martian regolith simulant. A Martian Garden has been built with the Martian soils, in which various vegetables have been seeded. Among the Fourier Transform infrared (FT-IR) spectra acquired for the plants, a high degree of similarity was observed, which indicates that the substrate ‘Martian regolith simulant of terrestrial soil’ does not significantly influence the structure of the radish, peas and bean leaves, stems, or roots. Nevertheless, the results of 1D 1H nuclear magnetic resonance (NMR) relaxometry indicate that the substrate presents a high influence on the water dynamics in plant pores at the level of roots, stems and leaves and in bound water. A Marsarium was designed and built, where all types of Martian and terrestrial soils were introduced, together with a family of ants. The ants adapted to the imposed conditions, as they dug tunnels in the soils.

Evaluating protocols for reproducible targeted metabolomics by NMR.

Metabolomics aims to study the downstream effects of variables like diet, environment, or disease on a given biological system. However, inconsistencies in sample preparation, data acquisition/processing protocols lead to reproducibility and accuracy concerns. A systematic study was conducted to assess how sample preparation methods and data analysis platforms affect metabolite susceptibility. A targeted panel of 25 metabolites was evaluated in 69 clinical metabolomics samples prepared following three different protocols: intact, ultrafiltration, and protein precipitation. The resulting metabolic profiles were characterized by 1D 1H nuclear magnetic resonance (NMR) spectroscopy and analyzed with Chenomx v8.3 and SMolESY software packages. Greater than 90% of the metabolites were extracted more efficiently using protein precipitation than filtration, which aligns with previously reported results. Additionally, analysis of data processing software suggests that metabolite concentrations were overestimated by Chenomx batch-fitting, which only appears reliable for determining relative fold changes rather than absolute quantification. However, an assisted-fit method provided sufficient guidance to achieve accurate results while avoiding a time-consuming fully manual-fitting approach. By combining our results with previous studies, we can now provide a list of 5 common metabolites [2-hydroxybutyrate (2-HB), choline, dimethylamine (DMA), glutamate, lactate] with a high degree of variability in reported fold changes and standard deviations that need careful consideration before being annotated as potential biomarkers. Our results show that sample preparation and data processing package critically impact clinical metabolomics study success. There is a clear need for an increased degree of standardization and harmonization of methods across the metabolomics community to ensure reliable outcomes.

Evaluation of nuclear magnetic resonance spectroscopy for characterisation and quantitation of water-soluble polymers in river water

Water-soluble polymers (WSPs) are commonly used in industrial, commercial, agricultural and pharmaceutical products and their molecular weights and concentrations vary considerably. Methods commonly used in the analysis of WSPs are often for pure products or formulations with only a few other high MW constituents. These methods, like size exclusion chromatography (SEC) or Gel Permeation Chromatography coupled with Mass Spectrometry (MS) can be frustrated by the impact of the necessary separation steps prior to identification and the limitations of MS when identifying and quantifying polymers. To that end, the employment of a Nuclear Magnetic Resonance (NMR) method to identify, characterize and quantify WSPs in the real-world is reported for the first time. Samples were taken from fourteen UK inland river sites, concentrated via air-drying, freeze-drying or vacuum-drying and analyzed using 1D 1H NMR and 2D 1H Diffusion Ordered Spectroscopy (DOSY) NMR analysis. Seven of the river sites showed the presence of polyethylene glycol (PEG) with a range of molecular weights, evidencing the application of these techniques in analysis of WSPs. Soil percolation models evidenced the proof of principle that these techniques can also be used for the detection of polyacrylamide (PAM) and polyacrylic acid (PAA). This work should better enable the evaluation of the biological impact of WSPs on aquatic organisms in future studies.

Metabolomic approaches for the detection of Listeria monocytogenes and Staphylococcus aureus in culture media

This study aimed to determine the changes in metabolites in the culture media of foodborne pathogens using nuclear magnetic resonance (NMR) spectroscopy. Extracellular metabolites in tryptic soy broth inoculated with none, Listeria monocytogenes ATCC 19111, and Staphylococcus aureus ATCC 29213 and 27213 at 37 °C were collected and analyzed by 1D 1H NMR and 2D 1H–13C heteronuclear single quantum coherence (HSQC) NMR. Partial least squares discriminant analysis revealed an excellent variable influence on projection (VIP) score and predictive ability among the different culture media metabolites. Based on presence/absence, five extracellular metabolites (cadaverine, histidine, nicotinate, putrescine, and succinate) were only detected in culture media containing pathogens. According to the pathogen species, L. monocytogenes and S. aureus ATCC 29213 could be distinguished based on changes in 11 metabolites (tyrosine, betaine, glycine, pyroglutamate, fumarate, alanine, putrescine, arginine, inosine, threonine, and sucrose). Moreover, different strains of S. aureus (ATCC 29213 and 27213) were distinguished using hypoxanthine and putrescine. Overall, extracellular metabolites in culture media could be used to detect the presence/absence, species, and even different strains of foodborne pathogens.

Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data.

Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1H NMR experiments offer good sensitivity at reasonable measurement times. Their subsequent data analysis requires sophisticated data preprocessing steps, including the extraction of NMR features corresponding to specific metabolites. We developed a novel 1D NMR feature extraction procedure, called Bucket Fuser (BF), which is based on a regularized regression framework with fused group LASSO terms. The performance of the BF procedure was demonstrated using three independent NMR datasets and was benchmarked against existing state-of-the-art NMR feature extraction methods. BF dynamically constructs NMR metabolite features, the widths of which can be adjusted via a regularization parameter. BF consistently improved metabolite signal extraction, as demonstrated by our correlation analyses with absolutely quantified metabolites. It also yielded a higher proportion of statistically significant metabolite features in our differential metabolite analyses. The BF algorithm is computationally efficient and it can deal with small sample sizes. In summary, the Bucket Fuser algorithm, which is available as a supplementary python code, facilitates the fast and dynamic extraction of 1D NMR signals for the improved detection of metabolic biomarkers.

NMRpQuant: an automated software for large scale urinary total protein quantification by one-dimensional 1H NMR profiles.

Summary 1H nuclear magnetic resonance (NMR) spectroscopy is an established bioanalytical technology for metabolic profiling of biofluids in both clinical and large-scale population screening applications. Recently, urinary protein quantification has been demonstrated using the same 1D 1H NMR experimental data captured for metabolic profiling. Here, we introduce NMRpQuant, a freely available platform that builds on these findings with both novel and further optimized computational NMR approaches for rigorous, automated protein urine quantification. The results are validated by interlaboratory comparisons, demonstrating agreement with clinical/biochemical methodologies, pointing at a ready-to-use tool for routine protein urinalyses.Availability and implementation NMRpQuant was developed on MATLAB programming environment. Source code and Windows/macOS compiled applications are available at https://github.com/pantakis/NMRpQuant, and working examples are available at https://doi.org/10.6084/m9.figshare.18737189.v1.Supplementary information Supplementary data are available at Bioinformatics online.

Identification of Metabolomic Biomarkers of Long-Term Stress Using NMR Spectroscopy in a Diving Duck.

Human-induced environmental changes that act as long-term stressors pose significant impacts on wildlife health. Energy required for maintenance or other functions may be re-routed towards coping with stressors, ultimately resulting in fluctuations in metabolite levels associated with energy metabolism. While metabolomics approaches are used increasingly to study environmental stressors, its use in studying stress in birds is in its infancy. We implanted captive lesser scaup (Aythya affinis) with either a biodegradable corticosterone (CORT) pellet to mimic the effects of a prolonged stressor or a placebo pellet. 1D 1H nuclear magnetic resonance (NMR) spectroscopy was performed on serum samples collected over 20 days after implant surgery. We hypothesized that CORT pellet-induced physiological stress would alter energy metabolism and result in distinct metabolite profiles in ducks compared with placebo (control). Quantitative targeted metabolite analysis revealed that metabolites related to energy metabolism: glucose, formate, lactate, glutamine, 3-hydroxybutyrate, ethanolamine, indole-3- acetate, and threonine differentiated ducks with higher circulatory CORT from controls on day 2. These metabolites function as substrates or intermediates in metabolic pathways related to energy production affected by elevated serum CORT. The use of metabolomics shows promise as a novel tool to identify and characterize physiological responses to stressors in wild birds.

NMR-based metabonomics reveals the dynamic effect of electro-acupuncture on central nervous system in gastric mucosal lesions (GML) rats

BackgroundGastric mucosal lesions (GML) are common in gastric diseases and seriously affect the quality of life. There are inevitable side effects in drug therapy. Acupuncture is an important part of traditional Chinese medicine. Electro-acupuncture (EA) has unique curative effect in treatment of GML. However, there are still few reports on the central mechanism of electro-acupuncture in treatment of GML. In this study, NMR metabonomics was used to explore the central metabolic change mechanism of electro-acupuncture in treatment of GML.MethodsSD rats were randomly divided into Control, GML and EA groups. According to different intervention time, each group was further divided into 3 subgroups. This study mainly established GML model rats by 75% ethanol. Dynamic expressions of metabolites in cerebral cortex and medulla were observed by 1D 1H Nuclear Magnetic Resonance (NMR) metabolomics, combined with gastric mucosal histopathological examination to evaluate the time-effect relationship of electro-acupuncture at Zusanli (ST36) and Liangmen (ST21) points for 1 day, 4 days and 7 days treatment of GML.ResultsThe results showed that the repair effect of electro-acupuncture on gastric mucosal injury was the most obvious in 4 days and stable in 7 days, and 4 days electro-acupuncture can effectively inhibit GML gastric mucosal inflammation and the expression of inflammatory cells. Meanwhile, the NMR spectrum results of medulla and cerebral cortex showed that, 21 potential metabolites were identified to participate in the mechanism of pathogenesis of GML and the regulation of electro-acupuncture, including 15 in medulla and 10 in cerebral cortex. Metabolic pathway analysis showed that the differential metabolites involved 19 metabolic pathways, which could be divided into energy, neurotransmitters, cells and cell membrane and antioxidation according to their functions. The correlation analysis of stomach, medulla and cerebral cortex shows that the stimulation signal of GML may reach the cerebral cortex from the stomach through medulla, and electro-acupuncture can treat GML by regulating the central nervous system (CNS).Conclusions4 days electro-acupuncture treatment can significantly improve gastric mucosal injury, and the curative effect tends to be stable in 7 days treatment. Meanwhile, the pathogenesis of GML and the efficacy of electro-acupuncture involve metabolic pathways such as energy, neurotransmitters, cells and antioxidation, and electro-acupuncture can treat GML by regulating CNS.

Elucidation of complex respiratory chains: a straightforward strategy to monitor electron transfer between cytochromes.

Cytochromes are electron transfer (ET) proteins essential in various biological systems, playing crucial roles in the respiratory chains of bacteria. These proteins are particularly abundant in electrogenic microorganisms and are responsible for the efficient delivery of electrons to the cells' exterior. The capability of sending electrons outside the cells open new avenues to be explored for emerging biotechnological applications in bioremediation, microbial electrosynthesis, and bioenergy fields. To develop these applications, it is critical to identify the different redox partners and to elucidate the stepwise ET along the respiratory paths. However, investigating direct ET events between proteins with identical features in nearly all spectroscopic techniques is extremely challenging. Nuclear magnetic resonance (NMR) spectroscopy offers the possibility to overcome this difficulty by analysing the alterations of the spectral signatures of each protein caused by electron exchange events. The uncrowded NMR spectral regions containing the heme resonances of the cytochromes display unique and distinct signatures in the reduced and oxidized states, which can be explored to monitor ET within the redox complex. In this study, we present a strategy for a fast and straightforward monitorization of ET between c-type cytochromes, using as model a triheme periplasmic cytochrome and a membrane-associated monoheme cytochrome from the electrogenic bacterium Geobacter sulfurreducens. The comparison between the 1D 1H NMR spectra obtained for samples containing the two cytochromes and for samples containing the individual proteins clearly demonstrated a unidirectional ET within the redox complex. This strategy provides a simple and straightforward means to elucidate complex biologic respiratory ET chains.

Deep Learning for Rapid Identification of Microbes Using Metabolomics Profiles.

Rapid detection of viable microbes remains a challenge in fields such as microbial food safety. We here present the application of deep learning algorithms to the rapid detection of pathogenic and non-pathogenic microbes using metabolomics data. Microbes were incubated for 4 h in a protein-free defined medium, followed by 1D 1H nuclear magnetic resonance (NMR) spectroscopy measurements. NMR spectra were analyzed by spectral binning in an untargeted metabolomics approach. We trained multilayer (“deep”) artificial neural networks (ANN) on the data and used the resulting models to predict spectra of unknown microbes. ANN predicted unknown microbes in this laboratory setting with an average accuracy of 99.2% when using a simple feature selection method. We also describe learning behavior of the employed ANN and the optimization strategies that worked well with these networks for our datasets. Performance was compared to other current data analysis methods, and ANN consistently scored higher than random forest models and support vector machines, highlighting the potential of deep learning in metabolomics data analysis.

Insights into the Conformation and Self-Association of a Concentrated Monoclonal Antibody using Isothermal Chemical Denaturation and Nuclear Magnetic Resonance

The purpose of this investigation was to highlight the utility of nuclear magnetic resonance (NMR) as a multi-attribute method for the characterization of therapeutic antibodies. In this case study, we compared results from isothermal chemical denaturation (ICD) and NMR with standard methods to relate conformational states of a model monoclonal antibody (mAb1) with protein-protein interactions (PPI) that lead to self – association in concentrated solutions. The increase in aggregation rate and relative viscosity for mAb1 was found to be both concentration and pH dependent. The free energy of unfolding (∆G⁰) from ICD and thermal analysis in dilute solutions indicated that although the native state predominated between pH 4 – pH 7, it was disrupted at the CH2 and unfolded noncooperatively under acidic conditions. One-dimensional (1D) 1H NMR and two-dimensional (2D) 13C-1H NMR performed, in concentrated solutions, confirmed that PPI between pH 4–7 occurred while mAb1 was in the native state. NMR corroborated that mAb1 maintained a dominant native state at formulation-relevant conditions at the tested pH range, had increased global molecular tumbling dynamics at lower pH and confirmed increased PPI at higher pH conditions. This report aligns and compares typical characterization of an IgG1 with assessment of structure by NMR and provided a more precise assessment and deeper insight into the conformation of an IgG1 in concentrated solutions.

Efficacy Of C-10 Massoialactone against-Multispecies Microbial Biofilm

Biofilm is a community of Microbes found in almost all habitats. They bind to the surface and become incorporated in the biomolecule-rich extracellular matrix. The matrix can fully Microorganisms are covered and rendered more immune to antibacterial agents, becoming a progressive infection source. Anti-biofilm compounds need to be discovered to deal with these biofilm-mediated infections. C-10 Massoialactone(C10H16O2), a significant component of MassoiaaromaticaBecc. The essential oil has demonstrated that antibacterial and anti-fungal properties are possible. This study aims to see how effective C-10 Massoialactone is against multi-species microbial biofilm of Candida albicans, Pseudomonas aeruginosa, and Escherichia coli. Massoia lactone It is derived from the essential oil of Massoiaaromatica bark. To isolate and explain these compounds, researchers used preparative thin layer chromatography (TLC), gas chromatography-mass spectrometry (GC-MS), with 1D-1H nuclear magnetic resonance (NMR) analysis. The impact of horse-tail lactone on several microbial biofilms was investigated. The biofilm structure was analyzed using a transmission electron microscope (TEM) and scanning electron microscope (SEM) the biofilm structure. GCMS and NMR results revealed the presence of C-10 Massoialactone (96.59 %). C-10 Massoialactoneshowed a dose-dependent activity in inhibiting biofilm formation as well as in breaking down established biofilms. Higher concentrations of C-10 Massoialactoneare required to inhibit the mature phase of the tested biofilm. TEM and SEM analysis showed apparent cell lysis in the biofilm in the presence of C-10 Massoialactone. C-10 Massoialactone can function because of its rich potential as a new anti-biofilm drug for various microbial biofilms.

A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D 1H NMR.

Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of 1H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional 1H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications.

Evaluation of the Metabolic Profile of Arabica Coffee via NMR in Relation to the Time and Temperature of the Roasting Procedure

Coffee is one of the most popular and consumed products in the world, with high nutritional value and economic importance. However, some factors can change the organoleptic properties of a coffee species, without causing significant damage such as loss of important components. The present study evaluated the chemical profile, via nuclear magnetic resonance (NMR), of the main biological properties and substances of the drink, verifying similarities in the composition of different types of arabica coffee made in different conditions, such as the roasting time and temperature. The main components were identified, using information from the literature and a database, and compared with the experimental data of 1D and 2D 1H NMR. The spectral data were analyzed and grouped via principal component analysis (PCA) using the Bruker Amix 3.9.14 software. 1H NMR was able to monitor the roasting process and qualify the intact bean and chemical profile of the coffee according to the roasting conditions. Due to the importance of the monitored components, the coffee species analyzed can be identified, along with the appearance of unwanted or adulterating compounds that are normally added to the product to reduce the cost of commercialization.

Comprehensive Assessment of Protein and Excipient Stability in Biopharmaceutical Formulations Using 1H NMR Spectroscopy.

Biopharmaceutical proteins are important drug therapies in the treatment of a range of diseases. Proteins, such as antibodies (Abs) and peptides, are prone to chemical and physical degradation, particularly at the high concentrations currently sought for subcutaneous injections, and so formulation conditions, including buffers and excipients, must be optimized to minimize such instabilities. Therefore, both the protein and small molecule content of biopharmaceutical formulations and their stability are critical to a treatment’s success. However, assessing all aspects of protein and small molecule stability currently requires a large number of analytical techniques, most of which involve sample dilution or other manipulations which may themselves distort sample behavior. Here, we demonstrate the application of 1H nuclear magnetic resonance (NMR) spectroscopy to study both protein and small molecule content and stability in situ in high-concentration (100 mg/mL) Ab formulations. We show that protein degradation (aggregation or fragmentation) can be detected as changes in 1D 1H NMR signal intensity, while apparent relaxation rates are specifically sensitive to Ab fragmentation. Simultaneously, relaxation-filtered spectra reveal the presence and degradation of small molecule components such as excipients, as well as changes in general solution properties, such as pH. 1H NMR spectroscopy can thus provide a holistic overview of biopharmaceutical formulation content and stability, providing a preliminary characterization of degradation and acting as a triaging step to guide further analytical techniques.

NMR-based metabolic profiling provides diagnostic and prognostic information in critically ill children with suspected infection

Sepsis, defined as life-threatening organ dysfunction caused by infection is difficult to distinguish clinically from infection or post-operative inflammation. We hypothesized that in a heterogeneous group of critically ill children, there would be different metabolic profiles between post-operative inflammation, bacterial and viral infection and infection with or without organ dysfunction. 1D 1H nuclear magnetic resonance spectra were acquired in plasma samples from critically ill children. We included children with bacterial (n = 25) and viral infection (n = 30) and controls (n = 58) (elective cardiac surgery without infection). Principal component analysis was used for data exploration and partial least squares discriminant analysis models for the differences between groups. Area under receiver operating characteristic curve (AUC) values were used to evaluate the models. Univariate analysis demonstrated differences between controls and bacterial and viral infection. There was excellent discrimination between bacterial and control (AUC = 0.94), and viral and control (AUC = 0.83), with slightly more modest discrimination between bacterial and viral (AUC = 0.78). There was modest discrimination (AUC = 0.73) between sepsis with organ dysfunction and infection with no organ dysfunction. In critically ill children, NMR metabolomics differentiates well between those with a post-operative inflammation but no infection, and those with infection (bacterial and viral), and between sepsis and infection.

Muscle and liver metabolomic signatures associated with residual feed intake in Nellore cattle

Identification of those cattle genetics with superior feed efficiency is necessary to increase the productivity of the entire beef industry. The aim of this study was to investigate metabolites profiling in the liver and muscle to identify physiological changes in animals in the livestock system with low or high residual feed intake. Liver and muscle were collected after slaughter from Nellore males, with high and low residual feed intake (H-RFI, n = 10 and L-RFI, n = 10, respectively). Metabolites were extracted with cold methanol/water solution (4:3 v/v). Metabolomics analyses were performed by 1D 1H nuclear magnetic resonance. Metabolites were identified using Chenomx and analysed using MetaboAnalyst 4.0. The residual feed intake classification did not change most of animal performance traits; however, animals with lower residual feed intake presented greater carcass dressing, lower carcass fat deposition. Sixty-three compounds were identified in the liver and 31 in muscle. Coexpression network analysis was carried out and identified four metabolite network modules correlated with the phenotypic traits, indicating common metabolites with a variate important projection score that influences residual feed intake. Metabolites in liver samples was correlated with energy and protein metabolism and on muscle with fat metabolism. Differences on energetic metabolism, including of carbohydrate- and fat-correlated compounds, according to residual feed intake can be assessed by importance of concentration of citrate, isocitrate, glucose-6-phosphate, nicotinamide adenine dinucleotide + hydrogen and creatine phosphate (in liver) and choline, glycine, glycerol, malonate, glucose-6-phosphate and 3-hydroxybutyrate (in muscle). Moreover, differences on protein metabolism, can be assessed by importance of creatinine, glutamine, urea and creatine phosphate concentration in liver and creatinine, creatine, anserine and carnosine in muscle. Animals with low residual feed intake presented changes in energy production, represented by carbohydrate and fatty acid metabolites, and decreased carcass fat deposition and protein turnover.

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.