987 results found
Nicholson J, 2013, Cancer knife sniffs out cancer cells (Science (221)), Science, Vol: 341, ISSN: 0036-8075
Cortes-Ciriano I, Koutsoukas A, Abian O, et al., 2013, Experimental validation of in silico target predictions on synergistic protein targets, MEDCHEMCOMM, Vol: 4, Pages: 278-288, ISSN: 2040-2503
Cantor GH, Beckonert O, Bollard ME, et al., 2013, Integrated Histopathological and Urinary Metabonomic Investigation of the Pathogenesis of Microcystin-LR Toxicosis, VETERINARY PATHOLOGY, Vol: 50, Pages: 159-171, ISSN: 0300-9858
Want EJ, Masson P, Michopoulos F, et al., 2013, Global metabolic profiling of animal and human tissues via UPLC-MS, NATURE PROTOCOLS, Vol: 8, Pages: 17-32, ISSN: 1754-2189
Rainville PD, Smith NW, Cowan D, et al., 2012, Investigation of basic mobile phases with positive ESI LC-MS for metabonomics studies, BIOANALYSIS, Vol: 4, Pages: 2833-2842, ISSN: 1757-6180
Nicholson JK, Holmes E, Kinross JM, et al., 2012, Metabolic phenotyping in clinical and surgical environments, NATURE, Vol: 491, Pages: 384-392, ISSN: 0028-0836
Posma JM, Garcia-Perez I, De Iorio M, et al., 2012, Subset Optimization by Reference Matching (STORM): An optimized statistical approach for recovery of metabolic biomarker structural information from ¹H NMR spectra of biofluids, Analytical Chemistry, Vol: 84, Pages: 10694-10701, ISSN: 0003-2700
We describe a new multivariate statistical approach to recover metabolite structure information from multiple 1H NMR spectra in population sample sets. SubseT Optimization by Reference Matching (STORM) was developed to select subsets of 1H NMR spectra that contain specific spectroscopic signatures of biomarkers differentiating between different human populations. STORM aims to improve the visualization of structural correlations in spectroscopic data using these reduced spectral subsets containing smaller numbers of samples than the number of variables (n<<p). We have used ‘statistical shrinkage’ to limit the number of false positive associations and to simplify the overall interpretation of the auto-correlation matrix. The STORM approach has been applied to findings from an on-going human Metabolome-Wide Association study on Body Mass Index to identify a biomarker metabolite present in a subset of the population. Moreover, we have shown how STORM improves the visualization of more abundant NMR peaks compared to a previously published method (STOCSY). STORM is a useful new tool for biomarker discovery in the ‘omic’ sciences that has a widespread applicability. It can be applied to any type of data, provided that there is interpretable correlation among variables, and can also be applied to data with more than 1 dimension (e.g. 2D-NMR spectra).
Holmes E, Li JV, Marchesi JR, et al., 2012, Gut Microbiota Composition and Activity in Relation to Host Metabolic Phenotype and Disease Risk, CELL METABOLISM, Vol: 16, Pages: 559-564, ISSN: 1550-4131
Anwar MA, Shalhoub J, Vorkas PA, et al., 2012, In-vitro Identification of Distinctive Metabolic Signatures of Intact Varicose Vein Tissue via Magic Angle Spinning Nuclear Magnetic Resonance Spectroscopy, EUROPEAN JOURNAL OF VASCULAR AND ENDOVASCULAR SURGERY, Vol: 44, Pages: 442-450, ISSN: 1078-5884
Kinross J, Nicholson JK, 2012, GUT MICROBIOTA Dietary and social modulation of gut microbiota in the elderly, NATURE REVIEWS GASTROENTEROLOGY & HEPATOLOGY, Vol: 9, Pages: 563-564, ISSN: 1759-5045
Cunningham K, Claus SP, Lindon JC, et al., 2012, Pharmacometabonomic Characterization of Xenobiotic and Endogenous Metabolic Phenotypes That Account for Inter-individual Variation in Isoniazid-Induced Toxicological Response, JOURNAL OF PROTEOME RESEARCH, Vol: 11, Pages: 4630-4642, ISSN: 1535-3893
Saric J, Want EJ, Duthaler U, et al., 2012, Systematic Evaluation of Extraction Methods for Multiplatform-Based Metabotyping: Application to the Fasciola hepatica Metabolome, ANALYTICAL CHEMISTRY, Vol: 84, Pages: 6963-6972, ISSN: 0003-2700
Muirhead LJ, Kinross J, FitzMaurice TS, et al., 2012, Surgical systems biology and personalized longitudinal phenotyping in critical care, Personalized Medicine, Vol: 9, Pages: 593-608, ISSN: 1741-0541
Zhao L, Nicholson JK, Lu A, et al., 2012, Targeting the Human Genome-Microbiome Axis for Drug Discovery: Inspirations from Global Systems Biology and Traditional Chinese Medicine, JOURNAL OF PROTEOME RESEARCH, Vol: 11, Pages: 3509-3519, ISSN: 1535-3893
The composition and activity of the gut microbiota codevelop with the host from birth and is subject to a complex interplay that depends on the host genome, nutrition, and life-style. The gut microbiota is involved in the regulation of multiple host metabolic pathways, giving rise to interactive host-microbiota metabolic, signaling, and immune-inflammatory axes that physiologically connect the gut, liver, muscle, and brain. A deeper understanding of these axes is a prerequisite for optimizing therapeutic strategies to manipulate the gut microbiota to combat disease and improve health.
Holmes E, Kinross J, Gibson GR, et al., 2012, Therapeutic Modulation of Microbiota-Host Metabolic Interactions, Science Translational Medicine, Vol: 4, Pages: 137rv6-137rv6, ISSN: 1946-6234
Merrifield CA, Lewis MC, Claus SP, et al., 2012, Weaning diet induces sustained metabolic phenotype shift in the pig and influences host response to Bifidobacterium lactis NCC2818, Gut
Background The process of weaning causes a major shift in intestinal microbiota and is a critical period for developing appropriate immune responses in young mammals.Objective To use a new systems approach to provide an overview of host metabolism and the developing immune system in response to nutritional intervention around the weaning period.Design Piglets (n=14) were weaned onto either an egg-based or soya-based diet at 3 weeks until 7 weeks, when all piglets were switched onto a fish-based diet. Half the animals on each weaning diet received Bifidobacterium lactis NCC2818 supplementation from weaning onwards. Immunoglobulin production from immunologically relevant intestinal sites was quantified and the urinary 1H NMR metabolic profile was obtained from each animal at post mortem (11 weeks).Results Different weaning diets induced divergent and sustained shifts in the metabolic phenotype, which resulted in the alteration of urinary gut microbial co-metabolites, even after 4 weeks of dietary standardisation. B lactis NCC2818 supplementation affected the systemic metabolism of the different weaning diet groups over and above the effects of diet. Additionally, production of gut mucosa-associated IgA and IgM was found to depend upon the weaning diet and on B lactis NCC2818 supplementation.Conclusion The correlation of urinary 1H NMR metabolic profile with mucosal immunoglobulin production was demonstrated, thus confirming the value of this multi-platform approach in uncovering non-invasive biomarkers of immunity. This has clear potential for translation into human healthcare with the development of urine testing as a means of assessing mucosal immune status. This might lead to early diagnosis of intestinal dysbiosis and with subsequent intervention, arrest disease development. This system enhances our overall understanding of pathologies under supra-organismal control.
Garcia-Perez I, Villaseñor A, Wijeyesekera A, et al., 2012, Urinary metabolic phenotyping the slc26a6 (chloride-oxalate exchanger) null mouse model, Journal of Proteome Research, Vol: 11, Pages: 4425-4435, ISSN: 1535-3893
The prevalence of renal stone disease is increasing, although it remains higher in men than in women when matched for age. While still somewhat controversial, several studies have reported an association between renal stone disease and hypertension, but this may be confounded by a shared link with obesity. However, independent of obesity, hyperoxaluria has been shown to be associated with hypertension in stone-formers and the most common type of renal stone is composed of calcium oxalate. The chloride-oxalate exchanger slc26a6 (also known as CFEX or PAT-1), located in the renal proximal tubule, was originally thought to have an important role in sodium homeostasis and thereby blood pressure control, but it has recently been shown to have a key function in oxalate balance by mediating oxalate secretion in the gut. We have applied two orthogonal analytical platforms (NMR spectroscopy and capillary-electrophoresis with UV detection) in parallel to characterize the urinary metabolic signatures related to the loss of the renal chloride-oxalate exchanger in slc26a6 null mice. Clear metabolic differentiation between the urinary profiles of the slc26a6 null and the wild type mice were observed using both methods, with the combination of NMR and CE-UV providing extensive coverage of the urinary metabolome. Key discriminating metabolites included oxalate, m-hydroxyphenylpropionylsulfate (m-HPPS), trimethylamine-N-oxide, glycolate and scyllo-inositol (higher in CFEX null mice) and hippurate, taurine, trimethylamine, and citrate (lower in slc26a6 null mice). In addition to the reduced efficiency of anion transport, several of these metabolites (hippurate, m-HPPS, methylamines) reflect alteration in gut microbial co-metabolic activities. Gender-related metabotypes were also observed in both wild type and slc26a6 null groups. Other urinary chemicals that showed a gender-specific pattern included trimethylamine, trimethylamine-N-oxide, citrate, spermidine, guanidinoacetate, and 2-
Mirnezami R, Kinross JM, Vorkas PA, et al., 2012, Implementation of Molecular Phenotyping Approaches in the Personalized Surgical Patient Journey, ANNALS OF SURGERY, Vol: 255, Pages: 881-889, ISSN: 0003-4932
Robinette SL, Holmes E, Nicholson JK, et al., 2012, Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations, GENOME MEDICINE, Vol: 4, ISSN: 1756-994X
Wong A, Jimenez B, Li X, et al., 2012, Evaluation of High Resolution Magic-Angle Coil Spinning NMR Spectroscopy for Metabolic Profiling of Nanoliter Tissue Biopsies, ANALYTICAL CHEMISTRY, Vol: 84, Pages: 3843-3848, ISSN: 0003-2700
Coen M, Goldfain-Blanc F, Rolland-Valognes G, et al., 2012, Pharmacometabonomic Investigation of Dynamic Metabolic Phenotypes Associated with Variability in Response to Galactosamine Hepatotoxicity, JOURNAL OF PROTEOME RESEARCH, Vol: 11, Pages: 2427-2440, ISSN: 1535-3893
Wijeyesekera A, Selman C, Barton RH, et al., 2012, Metabotyping of Long-Lived Mice using H-1 NMR Spectroscopy, Journal of Proteome Research, Vol: 11, Pages: 2224-2235, ISSN: 1535-3893
Significant advances in understanding aging have been achieved through studying model organisms with extended healthy lifespans. Employing 1H NMR spectroscopy, we characterized the plasma metabolic phenotype (metabotype) of three long-lived murine models: 30% dietary restricted (DR), insulin receptor substrate 1 null (Irs1–/–), and Ames dwarf (Prop1df/df). A panel of metabolic differences were generated for each model relative to their controls, and subsequently, the three long-lived models were compared to one another. Concentrations of mobile very low density lipoproteins, trimethylamine, and choline were significantly decreased in the plasma of all three models. Metabolites including glucose, choline, glycerophosphocholine, and various lipids were significantly reduced, while acetoacetate, d-3-hydroxybutyrate and trimethylamine-N-oxide levels were increased in DR compared to ad libitum fed controls. Plasma lipids and glycerophosphocholine were also decreased in Irs1–/– mice compared to controls, as were methionine and citrate. In contrast, high density lipoproteins and glycerophosphocholine were increased in Ames dwarf mice, as were methionine and citrate. Pairwise comparisons indicated that differences existed between the metabotypes of the different long-lived mice models. Irs1–/– mice, for example, had elevated glucose, acetate, acetone, and creatine but lower methionine relative to DR mice and Ames dwarfs. Our study identified several potential candidate biomarkers directionally altered across all three models that may be predictive of longevity but also identified differences in the metabolic signatures. This comparative approach suggests that the metabolic networks underlying lifespan extension may not be exactly the same for each model of longevity and is consistent with multifactorial control of the aging process.
Stebbing J, Sharma A, North B, et al., 2012, A metabolic phenotyping approach to understanding relationships between metabolic syndrome and breast tumour responses to chemotherapy, ANNALS OF ONCOLOGY, Vol: 23, Pages: 860-U2, ISSN: 0923-7534
Perez-Trujillo M, Lindon JC, Parella T, et al., 2012, Chiral Metabonomics: H-1 NMR-Based Enantiospecific Differentiation of Metabolites in Human Urine via Direct Cosolvation with beta-Cyclodextrin, ANALYTICAL CHEMISTRY, Vol: 84, Pages: 2868-2874, ISSN: 0003-2700
Benton HP, Want E, Keun HC, et al., 2012, Intra- and Interlaboratory Reproducibility of Ultra Performance Liquid Chromatography-Time-of-Flight Mass Spectrometry for Urinary Metabolic Profiling, ANALYTICAL CHEMISTRY, Vol: 84, Pages: 2424-2432, ISSN: 0003-2700
Gleeson MP, Modi S, Bender A, et al., 2012, The Challenges Involved in Modeling Toxicity Data In Silico: A Review, CURRENT PHARMACEUTICAL DESIGN, Vol: 18, Pages: 1266-1291, ISSN: 1381-6128
Kirchmair J, Williamson MJ, Tyzack JD, et al., 2012, Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms, JOURNAL OF CHEMICAL INFORMATION AND MODELING, Vol: 52, Pages: 617-648, ISSN: 1549-9596
Loo RL, Chan Q, Brown IJ, et al., 2012, A Comparison of Self-Reported Analgesic Use and Detection of Urinary Ibuprofen and Acetaminophen Metabolites by Means of Metabonomics The INTERMAP Study, AMERICAN JOURNAL OF EPIDEMIOLOGY, Vol: 175, Pages: 348-358, ISSN: 0002-9262
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