METHODS: The mid-stream urine was collected from 96 patients diagnosed with dengue fever at Penang General Hospital (PGH) and 50 healthy volunteers. Urine samples were analyzed with proton nuclear magnetic resonance (1H NMR) spectroscopy, followed by chemometric multivariate analysis. NMR signals highlighted in the orthogonal partial least square-discriminant analysis (OPLS-DA) S-plots were selected and identified using Human Metabolome Database (HMDB) and Chenomx Profiler. A highly predictive model was constructed from urine profile of dengue infected patients versus healthy individuals with the total R2Y (cum) value 0.935, and the total Q2Y (cum) value 0.832.
RESULTS: Data showed that dengue infection is related to amino acid metabolism, tricarboxylic acid intermediates cycle and β-oxidation of fatty acids. Distinct variations in certain metabolites were recorded in infected patients including amino acids, various organic acids, betaine, valerylglycine, myo-inositol and glycine.
CONCLUSION: Metabolomics approach provides essential insight into host metabolic disturbances following dengue infection.
Methods: NQC was synthesised and characterised using spectroscopic techniques. The compound was tested for its anti-inflammatory effect using Lipopolysaccharide from Escherichia coli (LPSEc) induced inflammation in mouse macrophages (RAW 264.7 cells). The effect of NQC on inflammatory cytokines was measured using enzyme-linked immune sorbent assay (ELISA). The Nrf2 activity of the compound NQC was determined using 'Keap1:Nrf2 Inhibitor Screening Assay Kit'. To obtain the insights on NQC's activity on Nrf2, molecular docking studies were performed using Schrödinger suite. The metabolic stability of NQC was determined using mouse, rat and human microsomes.
Results: NQC was found to be non-toxic at the dose of 50 µM on RAW 264.7 cells. NQC showed potent anti-inflammatory effect in an in vitro model of LPSEc stimulated murine macrophages (RAW 264.7 cells) with an IC50 value 26.13 ± 1.17 µM. NQC dose-dependently down-regulated the pro-inflammatory cytokines [interleukin (IL)-1β (13.27 ± 2.37 μM), IL-6 (10.13 ± 0.58 μM) and tumor necrosis factor (TNF)-α] (14.41 ± 1.83 μM); and inflammatory mediator, prostaglandin E2 (PGE2) with IC50 values, 15.23 ± 0.91 µM. Molecular docking studies confirmed the favourable binding of NQC at Kelch domain of Keap-1. It disrupts the Nrf2 interaction with kelch domain of keap 1 and its IC50 value was 4.21 ± 0.89 µM. The metabolic stability studies of NQC in human, rat and mouse liver microsomes revealed that it is quite stable with half-life values; 63.30 ± 1.73, 52.23 ± 0.81, 24.55 ± 1.13 min; microsomal intrinsic clearance values; 1.14 ± 0.31, 1.39 ± 0.87 and 2.96 ± 0.34 µL/min/g liver; respectively. It is observed that rat has comparable metabolic profile with human, thus, rat could be used as an in vivo model for prediction of pharmacokinetics and metabolism profiles of NQC in human.
Conclusion: NQC is a new class of NRF2 activator with potent in vitro anti-inflammatory activity and good metabolic stability.
METHODS: The case-control portion of the study was conducted in nine UK centers with men ages 50-69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.
RESULTS: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal.
CONCLUSIONS: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk.
IMPACT: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.
Methods: Proton nuclear magnetic resonance spectroscopy (1H NMR)-based metabolomics approach was used to investigate fecal and serum metabolome of rat model of IBS-D with and without HPM treatment.
Results: The current results showed that IBS-induced metabolic alterations in fecal and serum sample include higher level of threonine and UDP-glucose together with lower levels of aspartate, ornithine, leucine, isoleucine, proline, 2-hydroxy butyrate, valine, lactate, ethanol, arginine, 2-oxoisovalerate and bile acids. These altered metabolites potentially involve in impaired gut secretory immune system and intestinal inflammation, malabsorption of nutrients, and disordered metabolism of bile acids. Notably, the HPM treatment was found able to normalize the Bristol stool forms scale scores, fecal water content, plasma endotoxin level, and a number of IBS-induced metabolic changes.
Conclusions: These findings may provide useful insight into the molecular basis of IBS and mechanism of the HPM intervention.
METHODS: Warfarin relies on regular monitoring of International Normalized Ratio which is a standardized test to measure prothrombin time and appropriate dose adjustment. Pharmacometabonomics is a novel scientific field which deals with identification and quantification of the metabolites present in the metabolome using spectroscopic techniques such as Nuclear Magnetic Resonance (NMR). Pharmacometabonomics helps to indicate perturbation in the levels of metabolites in the cells and tissues due to drug or ingestion of any substance. NMR is one of the most widely-used spectroscopic techniques in metabolomics because of its reproducibility and speed.
RESULTS: There are many factors that influence the metabolism of warfarin, making changes in drug dosage common, and clinical factors like drug-drug interactions, dietary interactions and age explain for the most part the variability in warfarin dosing. Some studies have showed that pharmacogenetic testing for warfarin dosing does not improve health outcomes, and around 26% of the variation in warfarin dose requirements remains unexplained yet.
CONCLUSION: Many recent pharmacometabonomics studies have been conducted to identify novel biomarkers of drug therapies such as paracetamol, aspirin and simvastatin. Thus, a technique such as NMR based pharmacometabonomics to find novel biomarkers in plasma and urine might be useful to predict warfarin outcome.