Project description:Nuclear magnetic resonance (NMR) spectroscopy is an ideal platform for the metabolic analysis of biofluids due to its high reproducibility, nondestructiveness, nonselectivity in metabolite detection, and the ability to simultaneously quantify multiple classes of metabolites. Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. In this study, we combined multivariate pattern recognition (PR) analytical techniques with (1)H NMR spectroscopy to explore the metabolic profile of sera from TB patients. A total of 77 serum samples obtained from patients with TB (n = 38) and healthy controls (n = 39) were investigated. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was capable of distinguishing TB patients from controls and establishing a TB-specific metabolite profile. A total of 17 metabolites differed significantly in concentration between the two groups. Serum samples from TB patients were characterized by increased concentrations of 1-methylhistidine, acetoacetate, acetone, glutamate, glutamine, isoleucine, lactate, lysine, nicotinate, phenylalanine, pyruvate, and tyrosine, accompanied by reduced concentrations of alanine, formate, glycine, glycerolphosphocholine, and low-density lipoproteins relative to control subjects. Our study reveals the metabolic profile of sera from TB patients and indicates that NMR-based methods can distinguish TB patients from healthy controls. NMR-based metabolomics has the potential to be developed into a novel clinical tool for TB diagnosis or therapeutic monitoring and could contribute to an improved understanding of disease mechanisms.
Project description:ContextThe characterization of the urinary metabolome may yield biomarkers indicative of pancreatitis.ObjectivesWe establish a non-invasive technique to compare urinary metabolic profiles in patients with acute and chronic pancreatitis to healthy controls.MethodsUrine was obtained from healthy controls (HC, n=5), inpatients with mild acute pancreatitis (AP, n=5), and outpatients with chronic pancreatitis (CP, n=5). Proton nuclear magnetic resonance spectra were obtained for each sample. Metabolites were identified and quantified in each spectrum; resulting concentrations were normalized to account for differences in dilution among samples. Kruskal-Wallis test, post-hoc Mann-Whitney U tests, and principal component analysis were performed to identify metabolites that discriminate healthy controls, acute pancreatitis, and chronic pancreatitis.ResultsSixty metabolites were identified and quantified; five were found to differ significantly (P<0.05) among the three groups. Of these, citrate and adenosine remained significant after validation by random permutation. Principal component analysis demonstrated that healthy control urine samples can be differentiated from patients with chronic pancreatitis or acute pancreatitis; chronic pancreatitis patients could not be distinguished from acute pancreatitis patients.ConclusionsThis metabolomic investigation demonstrates that this non-invasive technique offers insight into the metabolic states of pancreatitis. Although the identified metabolites cannot conclusively be defined as biomarkers of disease, future studies will validate our findings in larger patient cohorts.
Project description:BackgroundUrinary tract infection (UTI) is one of the most common diagnoses in girls and women, and to a lesser extent in boys and men younger than 50 years. Escherichia coli, followed by Klebsiella spp. and Proteus spp., cause 75-90% of all infections. Infection of the urinary tract is identified by growth of a significant number of a single species in the urine, in the presence of symptoms. Urinary culture is an accurate diagnostic method but takes several hours or days to be carried out. Metabolomics analysis aims to identify biomarkers that are capable of speeding up diagnosis.MethodsUrine samples from 51 patients with a prior diagnosis of Escherichia coli-associated UTI, from 21 patients with UTI caused by other pathogens (bacteria and fungi), and from 61 healthy controls were analyzed. The 1H-NMR spectra were acquired and processed. Multivariate statistical models were applied and their performance was validated using permutation test and ROC curve.ResultsOrthogonal Partial Least Squares-discriminant Analysis (OPLS-DA) showed good separation (R2Y = 0.76, Q2=0.45, p < 0.001) between UTI caused by Escherichia coli and healthy controls. Acetate and trimethylamine were identified as discriminant metabolites. The concentrations of both metabolites were calculated and used to build the ROC curves. The discriminant metabolites identified were also evaluated in urine samples from patients with other pathogens infections to test their specificity.ConclusionsAcetate and trimethylamine were identified as optimal candidates for biomarkers for UTI diagnosis. The conclusions support the possibility of a fast diagnostic test for Escherichia coli-associated UTI using acetate and trimethylamine concentrations.
Project description:NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.
Project description:The aim of the study was to investigate differences in metabolic profiles between patients with major depressive disorder (MDD) with full remission (FR) and healthy controls (HCs). A total of 119 age-matched MDD patients with FR (n = 47) and HCs (n = 72) were enrolled and randomly split into training and testing sets. A 1H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomics approach was used to identify differences in expressions of plasma metabolite biomarkers. Eight metabolites, including histidine, succinic acid, proline, acetic acid, creatine, glutamine, glycine, and pyruvic acid, were significantly differentially-expressed in the MDD patients with FR in comparison with the HCs. Metabolic pathway analysis revealed that pyruvate metabolism via the tricarboxylic acid cycle linked to amino acid metabolism was significantly associated with the MDD patients with FR. An algorithm based on these metabolites employing a linear support vector machine differentiated the MDD patients with FR from the HCs with a predictive accuracy, sensitivity, and specificity of nearly 0.85. A metabolomics-based approach could effectively differentiate MDD patients with FR from HCs. Metabolomic signatures might exist long-term in MDD patients, with metabolic impacts on physical health even in patients with FR.
Project description:A customized metabolomics NMR database, TOCCATA, is introduced, which uses (13)C chemical shift information for the reliable identification of metabolites, their spin systems, and isomeric states. TOCCATA, whose information was derived from the BMRB and HMDB databases and the literature, currently contains 463 compounds and 801 spin systems, and it can be used through a publicly accessible web server. TOCCATA allows the identification of metabolites in the submillimolar concentration range from (13)C-(13)C total correlation spectroscopy experiments of complex mixtures, which is demonstrated for an Escherichia coli cell lysate, a carbohydrate mixture, and an amino acid mixture, all of which were uniformly (13)C-labeled.
Project description:BackgroundKetamine abuse has been linked to the system's damage, presenting with lower urinary tract symptoms (LUTS). While the pathogenesis of ketamine-induced urinary damage is not fully understood, fibrosis is believed to be a potential mechanism. A metabolomic investigation of the urinary metabolites in ketamine abuse was conducted to gain insights into its pathogenesis.MethodsA rat model of ketamine induced bladder fibrosis was established through tail vein injection of ketamine hydrochloride and control group was established through tail vein injection of the equivalent normal saline. Hematoxylin and eosin (H&E) staining and Masson trichrome staining were performed to evaluated bladder pathology. Urinary components were detected based on a metabolomic approach using ultra-high performance liquid tandem chromatography quadrupole time of flight mass spectrometry (UHPLC-QTOFMS platform). Orthogonal projections analyzed the data to latent structures discriminant analysis (OPLS-DA) and bioinformatics analysis.ResultsThe rat model of ketamine induced bladder fibrosis was confirmed through H&E and Masson trichrome staining. There were marked differences in the urinary metabolites between the experimental group and the control group. Compared to the control group, 16 kinds of differential metabolites were up-regulated and 102 differential metabolites were down-regulated in the urine samples of the ketamine group. Bioinformatics analysis revealed the related metabolic pathways.ConclusionsUsing a ketamine-induced bladder fibrosis rat model, this study identified the differential urinary metabolites expressed following ketamine treatment. These results provide vital clues for exploring the pathogenesis of ketamine-induced LUTS and may further contribute to the disease's diagnosis and treatment.
Project description:Metachromatic leukodystrophy (MLD) is a lysosomal storage disease caused by a deficiency of the arylsulfatase A (ARSA). ARSA deficiency leads to an accumulation of sulfatides primarily in the nervous system ultimately causing demyelination. With evolving therapeutic options, there is an increasing need for indicators to evaluate disease progression. Here, we report targeted metabolic urine profiling of 56 MLD patients including longitudinal sampling, using 1H (proton) nuclear magnetic resonance (NMR) spectroscopy. 1H-NMR urine spectra of 119 MLD samples and 323 healthy controls were analyzed by an in vitro diagnostics research (IVDr) tool, covering up to 50 endogenous and 100 disease-related metabolites on a 600-MHz IVDr NMR spectrometer. Quantitative data reports were analyzed regarding age of onset, clinical course, and therapeutic intervention. The NMR data reveal metabolome changes consistent with a multiorgan affection in MLD patients in comparison to controls. In the MLD cohort, N-acetylaspartate (NAA) excretion in urine is elevated. Early onset MLD forms show a different metabolic profile suggesting a metabolic shift toward ketogenesis in comparison to late onset MLD and controls. In samples of juvenile MLD patients who stabilize clinically after hematopoietic stem cell transplantation (HSCT), the macrophage activation marker neopterin is elevated. We were able to identify different metabolic patterns reflecting variable organ disturbances in MLD, including brain and energy metabolism and inflammatory processes. We suggest NAA in urine as a quantitative biomarker for neurodegeneration. Intriguingly, elevated neopterin after HSCT supports the hypothesis that competent donor macrophages are crucial for favorable outcome.
Project description:Tuberculosis (TB) is a communicable disease of major global importance and causes metabolic disorder of the patients. In a previous study, we found that the plasma metabolite profile of TB patients differs from that of healthy control subjects based on nuclear magnetic resonance (NMR) spectroscopy. In order to evaluate the TB specificity of the metabolite profile, a total of 110 patients, including 40 with diabetes, 40 with malignancy, and 30 with community-acquired pneumonia (CAP), assessed by NMR spectroscopy, and compared to those of patients with TB. Based on the orthogonal partial least-squares discriminant analysis (OPLS-DA), the metabolic profiles of these diseases were significant different, as compared to the healthy controls and TB patients, respectively. The score plots of the OPLS-DA model demonstrated that TB was easily distinguishable from diabetes, CAP and malignancy. Plasma levels of ketone bodies, lactate, and pyruvate were increased in TB patient compared to healthy control, but lower than CAP and malignancy. We conclude that the metabolic profiles were TB-specific and reflected MTB infection. Our results strongly support the NMR spectroscopy-based metabolomics could contribute to an improved understanding of disease mechanisms and may offer clues to new TB clinic diagnosis and therapies.