Publications
742 results found
Coen M, Holmes E, Nicholson JK, et al., 2008, The Development of a Metabonomic-Based Drug Safety Testing Paradigm, Pages: 309-343
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- Citations: 2
Azmi J, Connelly J, Holmes E, et al., 2008, Characterization of the biochemical effects of 1-nitronaphthalene in rats using global metabolic profiling by NMR spectroscopy and pattern recognition, BIOMARKERS, Vol: 10, Pages: 401-416, ISSN: 1354-750X
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- Citations: 33
Maher AD, Crockford D, Toft H, et al., 2008, Optimization of human plasma <SUP>1</SUP>H NMR spectroscopic data processing for high-throughput metabolic phenotyping studies and detection of insulin resistance related to type 2 diabetes, ANALYTICAL CHEMISTRY, Vol: 80, Pages: 7354-7362, ISSN: 0003-2700
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- Citations: 55
Huq SM, Oldapo MNJ, Wang Y, et al., 2008, High glucose and low lactate: a metabolic signature of hypertension in human serum?, 13th Annual Meeting of the European-Council-for-Cardiovascular-Research, Publisher: LIPPINCOTT WILLIAMS & WILKINS, Pages: 758-759, ISSN: 0194-911X
Claus SP, Tsang TM, Wang Y, et al., 2008, Systemic multicompartmental effects of the gut microbiome on mouse metabolic phenotypes, Molecular Systems Biology, Vol: 4
To characterize the impact of gut microbiota on host metabolism, we investigated the multicompartmental metabolic profiles of a conventional mouse strain (C3H/HeJ) (n=5) and its germ-free (GF) equivalent (n=5). We confirm that the microbiome strongly impacts on the metabolism of bile acids through the enterohepatic cycle and gut metabolism (higher levels of phosphocholine and glycine in GF liver and marked higher levels of bile acids in three gut compartments). Furthermore we demonstrate that (1) well-defined metabolic differences exist in all examined compartments between the metabotypes of GF and conventional mice: bacterial co-metabolic products such as hippurate (urine) and 5-aminovalerate (colon epithelium) were found at reduced concentrations, whereas raffinose was only detected in GF colonic profiles. (2) The microbiome also influences kidney homeostasis with elevated levels of key cell volume regulators (betaine, choline, myo-inositol and so on) observed in GF kidneys. (3) Gut microbiota modulate metabotype expression at both local (gut) and global (biofluids, kidney, liver) system levels and hence influence the responses to a variety of dietary modulation and drug exposures relevant to personalized health-care investigations.
Bohus E, Coen M, Keun HC, et al., 2008, Temporal metabonomic modeling of L-arginine-induced exocrine pancreatitis, JOURNAL OF PROTEOME RESEARCH, Vol: 7, Pages: 4435-4445, ISSN: 1535-3893
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- Citations: 47
Holmes E, Wilson ID, Nicholson JK, 2008, Metabolic phenotyping in health and disease, CELL, Vol: 134, Pages: 714-717, ISSN: 0092-8674
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- Citations: 571
Nicholson JK, Holmes E, Elliott P, 2008, The metabolome-wide association study: A new look at human disease risk factors, JOURNAL OF PROTEOME RESEARCH, Vol: 7, Pages: 3637-3638, ISSN: 1535-3893
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- Citations: 65
Li JV, Wang Y, Saric J, et al., 2008, Global metabolic responses of NMRI mice to an experimental <i>Plasmodium berghei</i> infection, JOURNAL OF PROTEOME RESEARCH, Vol: 7, Pages: 3948-3956, ISSN: 1535-3893
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- Citations: 61
Papaspyridonos K, Garcia-Perez I, Angulo S, et al., 2008, Fingerprinting of human bile during liver transplantation by capillary electrophoresis, JOURNAL OF SEPARATION SCIENCE, Vol: 31, Pages: 3058-3064, ISSN: 1615-9306
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- Citations: 13
Khaitovich P, Lockstone HE, Wayland MT, et al., 2008, Metabolic changes in schizophrenia and human brain evolution, Genome Biology, Vol: 9, Pages: 1-11, ISSN: 1474-7596
BackgroundDespite decades of research, the molecular changes responsible for the evolution of human cognitive abilities remain unknown. Comparative evolutionary studies provide detailed information about DNA sequence and mRNA expression differences between humans and other primates but, in the absence of other information, it has proved very difficult to identify molecular pathways relevant to human cognition.ResultsHere, we compare changes in gene expression and metabolite concentrations in the human brain and compare them to the changes seen in a disorder known to affect human cognitive abilities, schizophrenia. We find that both genes and metabolites relating to energy metabolism and energy-expensive brain functions are altered in schizophrenia and, at the same time, appear to have changed rapidly during recent human evolution, probably as a result of positive selection.ConclusionOur findings, along with several previous studies, suggest that the evolution of human cognitive abilities was accompanied by adaptive changes in brain metabolism, potentially pushing the human brain to the limit of its metabolic capabilities.
Holmes E, Loo RL, Cloarec O, et al., 2008, Detection of urinary drug metabolite (xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy (vol 79, pg 2629, 2007), ANALYTICAL CHEMISTRY, Vol: 80, Pages: 6142-6143, ISSN: 0003-2700
Kinross JM, von Roon AC, Holmes E, et al., 2008, The human gut microbiome: implications for future health care., Curr Gastroenterol Rep, Vol: 10, Pages: 396-403
In their intestine, humans possess an "extended genome" of millions of microbial genes-the microbiome. Because this complex symbiosis influences host metabolism, physiology, and gene expression, it has been proposed that humans are complex biologic "superorganisms." Advances in microbiologic analysis and systems biology are now beginning to implicate the gut microbiome in the etiology of localized intestinal diseases such as the irritable bowel syndrome, inflammatory bowel disease, and colon cancer. These approaches also suggest possible links between the gut and previously unassociated systemic conditions such as type 2 diabetes and obesity. The elucidation of the intestinal microbiome is therefore likely to underpin future disease prevention strategies, personalized health care regimens, and the development of novel therapeutic interventions. This review summarizes the research that is defining our understanding of the intestinal microbiome and highlights future areas of research in gastroenterology and human health in which the intestinal microbiome will play a significant role.
Saric J, Li JV, Wang Y, et al., 2008, Metabolic profiling of an Echinostoma caproni infection in the mouse for biomarker discovery., PLoS Neglected Tropical Diseases, Vol: 2, Pages: 1-15, ISSN: 1935-2727
BackgroundMetabolic profiling holds promise with regard to deepening our understanding of infection biology and disease states. The objectives of our study were to assess the global metabolic responses to an Echinostoma caproni infection in the mouse, and to compare the biomarkers extracted from different biofluids (plasma, stool, and urine) in terms of characterizing acute and chronic stages of this intestinal fluke infection.Methodology/Principal FindingsTwelve female NMRI mice were infected with 30 E. caproni metacercariae each. Plasma, stool, and urine samples were collected at 7 time points up to day 33 post-infection. Samples were also obtained from non-infected control mice at the same time points and measured using 1H nuclear magnetic resonance (NMR) spectroscopy. Spectral data were subjected to multivariate statistical analyses. In plasma and urine, an altered metabolic profile was already evident 1 day post-infection, characterized by reduced levels of plasma choline, acetate, formate, and lactate, coupled with increased levels of plasma glucose, and relatively lower concentrations of urinary creatine. The main changes in the urine metabolic profile started at day 8 post-infection, characterized by increased relative concentrations of trimethylamine and phenylacetylglycine and lower levels of 2-ketoisocaproate and showed differentiation over the course of the infection.Conclusion/SignificanceThe current investigation is part of a broader NMR-based metabonomics profiling strategy and confirms the utility of this approach for biomarker discovery. In the case of E. caproni, a diagnosis based on all three biofluids would deliver the most comprehensive fingerprint of an infection. For practical purposes, however, future diagnosis might aim at a single biofluid, in which case urine would be chosen for further investigation, based on quantity of biomarkers, ease of sampling, and the degree of differentiation from the non-infected control group.
Richards SE, Wang Y, Lawler D, et al., 2008, Self modeling curve resolution recovery of temporal metabolite signal modulation in NMR spectroscopic data sets: Application to a life-long caloric restriction study in dogs, ANALYTICAL CHEMISTRY, Vol: 80, Pages: 4876-4885, ISSN: 0003-2700
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- Citations: 22
Martin F-PJ, Wang Y, Sprenger N, et al., 2008, Top-down systems biology integration of conditional prebiotic modulated transgenomic interactions in a humanized microbiome mouse model, MOLECULAR SYSTEMS BIOLOGY, Vol: 4, ISSN: 1744-4292
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- Citations: 72
Holmes E, Loo RL, Stamler J, et al., 2008, Human metabolic phenotype diversity and its association with diet and blood pressure, NATURE, Vol: 453, Pages: 396-U50, ISSN: 0028-0836
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- Citations: 812
Bictash M, Holmes E, Keun H, et al., 2008, Exploring the Contribution of Metabolic Profiling to Epidemiological Studies, Pages: 167-180
Wang Y, Utzinger J, Saric J, et al., 2008, Global metabolic responses of mice to Trypanosoma brucei brucei infection, Proceedings of the National Academy of Sciences USA, Vol: 105, Pages: 6127-6132
Barton RH, Nicholson JK, Elliott P, et al., 2008, High-throughput <SUP>1</SUP>H NMR-based metabolic analysis of human serum and urine for large-scale epidemiological studies:: validation study, INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, Vol: 37, Pages: 31-40, ISSN: 0300-5771
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- Citations: 98
Bang J-W, Crockford DJ, Holmes E, et al., 2008, Integrative top-down system metabolic modeling in experimental disease states via data-driven bayesian methods (vol 7, pg 497, 2008), JOURNAL OF PROTEOME RESEARCH, Vol: 7, Pages: 1352-1352, ISSN: 1535-3893
Scanlan PD, Shanahan F, Clune Y, et al., 2008, Culture-independent analysis of the gut microbiota in colorectal cancer and polyposis, ENVIRONMENTAL MICROBIOLOGY, Vol: 10, Pages: 789-798, ISSN: 1462-2912
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- Citations: 178
Fearnside JF, Dumas M-E, Rothwell AR, et al., 2008, Phylometabonomic Patterns of Adaptation to High Fat Diet Feeding in Inbred Mice, PLOS ONE, Vol: 3, ISSN: 1932-6203
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- Citations: 83
Bylesjo M, Rantalainen M, Nicholson JK, et al., 2008, K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space, BMC BIOINFORMATICS, Vol: 9, ISSN: 1471-2105
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- Citations: 65
Rantalainen M, Cloarec O, Ebbels TMD, et al., 2008, Piecewise multivariate modelling of sequential metabolic profiling data, BMC BIOINFORMATICS, Vol: 9, ISSN: 1471-2105
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- Citations: 20
Keun HC, Athersuch TJ, Beckonert O, et al., 2008, Heteronuclear <SUP>19</SUP>F-<SUP>1</SUP>H statistical total correlation spectroscopy as a tool in drug metabolism:: Study of flucloxacillin biotransformation, ANALYTICAL CHEMISTRY, Vol: 80, Pages: 1073-1079, ISSN: 0003-2700
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- Citations: 47
Wang Y, Cloarec O, Tang H, et al., 2008, Magic angle spinning NMR and <SUP>1</SUP>H-<SUP>31</SUP>P heteronuclear statistical total correlation spectroscopy of intact human gut biopsies, ANALYTICAL CHEMISTRY, Vol: 80, Pages: 1058-1066, ISSN: 0003-2700
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- Citations: 47
Li M, Wang B, Zhang M, et al., 2008, Symbiotic gut microbes modulate human metabolic phenotypes, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 105, Pages: 2117-2122, ISSN: 0027-8424
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- Citations: 830
Saidin NA, Takayama H, Holmes E, et al., 2008, Cytotoxicity of extract of Malaysian <i>Kratom</i> and its dominant alkaloid mitragynine, on human cell lines, 7th Annual Oxford International Conference on the Science of Botanicals/4th Interim Meeting of the American-Society-of-Pharmacognosy, Publisher: GEORG THIEME VERLAG KG, Pages: 348-348, ISSN: 0032-0943
Bang J-W, Crockford DJ, Holmes E, et al., 2008, Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods., J Proteome Res, Vol: 7, Pages: 497-503, ISSN: 1535-3893
Multivariate metabolic profiles from biofluids such as urine and plasma are highly indicative of the biological fitness of complex organisms and can be captured analytically in order to derive top-down systems biology models. The application of currently available modeling approaches to human and animal metabolic pathway modeling is problematic because of multicompartmental cellular and tissue exchange of metabolites operating on many time scales. Hence, novel approaches are needed to analyze metabolic data obtained using minimally invasive sampling methods in order to reconstruct the patho-physiological modulations of metabolic interactions that are representative of whole system dynamics. Here, we show that spectroscopically derived metabolic data in experimental liver injury studies (induced by hydrazine and alpha-napthylisothiocyanate treatment) can be used to derive insightful probabilistic graphical models of metabolite dependencies, which we refer to as metabolic interactome maps. Using these, system level mechanistic information on homeostasis can be inferred, and the degree of reversibility of induced lesions can be related to variations in the metabolic network patterns. This approach has wider application in assessment of system level dysfunction in animal or human studies from noninvasive measurements.
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