Project description:Background and purposeGlyphosate-based herbicides, extensively utilized worldwide, raise concerns regarding potential human risks due to the detection of glyphosate (GLY) in human body fluids. This study aims to address critical knowledge gaps regarding whether GLY undergoes metabolism in humans, particularly considering the limited information available on human metabolism.Experimental approachThe study investigated GLY and its metabolites in eight amenity horticultural workers using proton nuclear magnetic resonance (1H-NMR) data analysis. Multiple spot urine samples were collected before and after herbicide applications.Key resultsFindings reveal the presence of GLY and its metabolites (AMPA, formaldehyde, sarcosine, glyoxylic acid, and methylamine). Results demonstrate a moderate correlation between median GLY concentration and its metabolites within the studied population.ConclusionPersuasive evidence suggests the potential metabolism of GLY in humans. 1H-NMR data analysis might be a promising technique for determining the metabolism of GLY in humans, offering valuable insights into urinary excretion patterns.
Project description:Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.
Project description:Biofluid biomarkers of age-related macular degeneration (AMD) are still lacking, and their identification is challenging. Metabolomics is well-suited to address this need, and urine is a valuable accessible biofluid. This study aimed to characterize the urinary metabolomic signatures of patients with different stages of AMD and a control group (>50 years). It was a prospective, cross-sectional study, where subjects from two cohorts were included: 305 from Coimbra, Portugal (AMD patients n = 252; controls n = 53) and 194 from Boston, United States (AMD patients n = 147; controls n = 47). For all participants, we obtained color fundus photographs (for AMD staging) and fasting urine samples, which were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy. Our results revealed that in both cohorts, urinary metabolomic profiles differed mostly between controls and late AMD patients, but important differences were also found between controls and subjects with early AMD. Analysis of the metabolites responsible for these separations revealed that, even though distinct features were observed for each cohort, AMD was in general associated with depletion of excreted citrate and selected amino acids at some stage of the disease, suggesting enhanced energy requirements. In conclusion, NMR metabolomics enabled the identification of urinary signals of AMD and its severity stages, which might represent potential metabolomic biomarkers of the disease.
Project description:Ketosis is associated with high milk yield during lactating or insufficient feed intake in lactating dairy cows. However, few studies have been conducted on the metabolomics of ketosis in Korean lactating dairy cows. The present study aimed to investigate the serum and urine metabolites profiling of lactating dairy cows through proton nuclear magnetic resonance (1H-NMR) spectroscopy and comparing those between healthy (CON) and subclinical ketosis (SCK) groups. Six lactating dairy cows were categorized into CON and SCK groups. All experimental Holstein cows were fed total mixed ration. Serum and urine samples were collected from the jugular vein of the neck and by hand sweeping the perineum, respectively. The metabolites in the serum and urine were determined using 1H-NMR spectroscopy. Identification and quantification of metabolites was performed by Chenomx NMR Suite 8.4 software. Metabolites statistical analysis was performed by Metaboanalyst version 5.0 program. In the serum, the acetoacetate level was significantly (p < 0.05) higher in the SCK group than in the CON group, and whereas acetate, galactose and pyruvate levels tended to be higher. CON group had significantly (p < 0.05) higher levels of 5-aminolevulinate and betaine. Indole-3-acetate, theophylline, p-cresol, 3-hydroxymandelate, gentisate, N-acetylglucosamine, N-nitrosodimethylamine, xanthine and pyridoxine levels were significantly (p < 0.05) higher in the urine of the SCK group than that in the CON group, which had higher levels of homogentisate, ribose, gluconate, ethylene glycol, maltose, 3-methyl-2-oxovalerate and glycocholate. Some significantly (p < 0.05) different metabolites in the serum and urine were associated with ketosis diseases, inflammation, energy balance and body weight. This study will be contributed useful a future ketosis metabolomics studies in Korea.
Project description:Exosomes are a subset of secreted lipid envelope-encapsulated extracellular vesicles (EVs) of 50-150 nm diameter that can transfer cargo from donor to acceptor cells. In the current purification protocols of exosomes, many smaller and larger nanoparticles such as lipoproteins, exomers and microvesicles are typically co-isolated as well. Particle size distribution is one important characteristics of EV samples, as it reflects the cellular origin of EVs and the purity of the isolation. However, most of the physicochemical analytical methods today cannot illustrate the smallest exosomes and other small particles like the exomers. Here, we demonstrate that diffusion ordered spectroscopy (DOSY) nuclear magnetic resonance (NMR) method enables the determination of a very broad distribution of extracellular nanoparticles, ranging from 1 to 500 nm. The range covers sizes of all particles included in EV samples after isolation. The method is non-invasive, as it does not require any labelling or other chemical modification. We investigated EVs secreted from milk as well as embryonic kidney and renal carcinoma cells. Western blot analysis and immuno-electron microscopy confirmed expression of exosomal markers such as ALIX, TSG101, CD81, CD9, and CD63 in the EV samples. In addition to the larger particles observed by nanoparticle tracking analysis (NTA) in the range of 70-500 nm, the DOSY distributions include a significant number of smaller particles in the range of 10-70 nm, which are visible also in transmission electron microscopy images but invisible in NTA. Furthermore, we demonstrate that hyperpolarized chemical exchange saturation transfer (Hyper-CEST) with 129Xe NMR indicates also the existence of smaller and larger nanoparticles in the EV samples, providing also additional support for DOSY results. The method implies also that the Xe exchange is significantly faster in the EV pool than in the lipoprotein/exomer pool.
Project description:A convenient and fast method for quantifying urea in biofluids is demonstrated using NMR analysis and the solvent water signal as a concentration reference. The urea concentration can be accurately determined with errors less than 3% between 1 mM and 50 mM, and less than 2% above 50 mM in urine and serum. The method is promising for various applications with advantages of simplicity, high accuracy, and fast non-destructive detection. With an ability to measure other metabolites simultaneously, this NMR method is also likely to find applications in metabolic profiling and system biology.
Project description:Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs and a week earlier than HPMRS alone. Our model accurately predicted in vivo biomarkers from HPMRS, and the results inferred biological relevance. Additionally, the model showed potential for examining treatment effects. Our model successfully detected tumor progression two weeks earlier than conventional MRIs and accurately predicted in vivo biomarkers using ex vivo information such as conventional MRIs, HPMRS, and tumor size data. The accuracy of these predictions is consistent with biological relevance.
Project description:Details are given of the n.m.r. spectra of mycobactins F, H, M, N, P, S and T, and resonances are ascribed to all the protons in these molecules. A simplified system is described for identifying known mycobactins by the n.m.r. spectrum alone. This method will not distinguish mycobactins S and T, whose nuclei differ only in the configuration at an asymmetric centre.
Project description:The utility of low-resolution 1H-NMR analysis for the identification of biomarkers provided evidence for rapid biochemical diagnoses of organic acidemia and aminoacidopathy. 1H-NMR, with a sensitivity expected for a field strength of 400 MHz at 64 scans was used to establish the metabolomic urine sample profiles of an infant population diagnosed with small molecule Inborn Errors of Metabolism (smIEM) compared to unaffected individuals. A qualitative differentiation of the 1H-NMR spectral profiles of urine samples obtained from individuals affected by different organic acidemias and aminoacidopathies was achieved in combination with GC-MS. The smIEM disorders investigated in this study included phenylalanine metabolism; isovaleric, propionic, 3-methylglutaconicm and glutaric type I acidemia; and deficiencies in medium chain acyl-coenzyme and holocarboxylase synthase. The observed metabolites were comparable and similar to those reported in the literature, as well as to those detected with higher-resolution NMR. In this study, diagnostic marker metabolites were identified for the smIEM disorders. In some cases, changes in metabolite profiles differentiated post-treatments and follow-ups while allowing for the establishment of different clinical states of a biochemical disorder. In addition, for the first time, a 1H-NMR-based biomarker profile was established for holocarboxylase synthase deficiency spectrum.
Project description:ObjectiveThe aim of the study was to conduct metabolic profiling of dairy cattle serum and urine using proton nuclear magnetic resonance (1H-NMR) spectroscopy and to compare the results obtained with those of other dairy cattle herds worldwide so as to provide a basic dataset to facilitate research on metabolites in serum and urine.MethodsSix dairy cattle were used in this study; all animals were fed the same diet, which was composed of total mixed ration; the fed amounts were based on voluntary intake. Blood from the jugular neck vein of each steer was collected at the same time using a separate serum tube. Urine samples were collected by hand sweeping the perineum. The metabolites were determined by 1H-NMR spectroscopy, and the obtained data were statistically analyzed by performing principal component analysis, partial least squares-discriminant analysis, variable importance in projection scores, and metabolic pathway data using Metaboanalyst 4.0.ResultsThe total number of metabolites in the serum and urine was measured to be 115 and 193, respectively, of which 47 and 81, respectively were quantified. Lactate (classified as an organic acid) and urea (classified as an aliphatic acylic compound) exhibited the highest concentrations in serum and urine, respectively. Some metabolites that have been associated with diseases such as ketosis, bovine respiratory disease, and metritis, and metabolites associated with heat stress were also found in the serum and urine samples.ConclusionThe metabolites measured in the serum and urine could potentially be used to detect diseases and heat stress in dairy cattle. The results could also be useful for metabolomic research on the serum and urine of ruminants in Korea.