ORCID Profile
0000-0001-7469-2845
Current Organisations
The University of Auckland
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Publisher: American Chemical Society (ACS)
Date: 27-02-2012
DOI: 10.1021/PR2010154
Publisher: Springer Science and Business Media LLC
Date: 09-08-2018
DOI: 10.1038/S41591-018-0169-5
Abstract: In the version of this article originally published, the received date was missing. It should have been listed as 2 January 2018. The error has been corrected in the HTML and PDF versions of this article.
Publisher: Springer Science and Business Media LLC
Date: 25-06-2018
Publisher: American Chemical Society (ACS)
Date: 08-10-2014
DOI: 10.1021/PR500161W
Abstract: We have investigated the urinary and plasma metabolic phenotype of acute pancreatitis (AP) patients presenting to the emergency room at a single center London teaching hospital with acute abdominal pain using (1)H NMR spectroscopy and multivariate modeling. Patients were allocated to either the AP (n = 15) or non-AP patients group (all other causes of abdominal pain, n = 21) on the basis of the national guidelines. Patients were assessed for three clinical outcomes: (1) diagnosis of AP, (2) etiology of AP caused by alcohol consumption and cholelithiasis, and (3) AP severity based on the Glasgow score. S les from AP patients were characterized by high levels of urinary ketone bodies, glucose, plasma choline and lipid, and relatively low levels of urinary hippurate, creatine and plasma-branched chain amino acids. AP could be reliably identified with a high degree of sensitivity and specificity (OPLS-DA model R(2) = 0.76 and Q(2)Y = 0.59) using panel of discriminatory biomarkers consisting of guanine, hippurate and creatine (urine), and valine, alanine and lipoproteins (plasma). Metabolic phenotyping was also able to distinguish between cholelithiasis and colonic inflammation among the heterogeneous non-AP group. This work has demonstrated that combinatorial biomarkers have a strong diagnostic and prognostic potential in AP with relevance to clinical decision making in the emergency unit.
Publisher: Oxford University Press (OUP)
Date: 08-02-2008
DOI: 10.1093/IJE/DYM284
Abstract: Metabolic profiling of biofluid specimens is an established method for investigating disease states in clinical studies but is only recently being applied to large-scale human population studies. As part of protocol development for the UK Biobank study, a (1)H nuclear magnetic resonance (NMR)-based metabonomic analysis of specimen storage effects and analytical reproducibility was carried out using urine and serum specimens from 40 volunteers. Aliquots of each specimen were stored for t = 0 and t = 24 h at 4 degrees C prior to freezing, and in the case of serum s les for a further 12 h (t = 36), to determine whether the storage times affected specimen composition and quality. A blinded split-specimen matching exercise was implemented to assign candidate spectral pairs stored for different times using multivariate statistical analysis of the NMR data. Using a chemometric strategy, split specimens at time t = 0 and t = 24 or 36 h after storage at 4 degrees C were easily paired and the split-specimen matching task was reduced to a workable size. (1)H NMR profiling established that the t = 24 h urine and serum groups showed no systematic metabolite changes, indicating biochemical stability. Some small differences in serum specimens stored for t = 36 h at 4 degrees C were detectable only by multivariate analysis, and were attributed to generalized alterations in proteins and protein fragments, and possibly trimethylamine-N-oxide. No other specific metabolite was implicated. For the purposes of NMR-based analysis, storage of urine and serum for up to t = 24 h at 4 degrees C does not detectably affect the metabolic profile and the methodology is robust. Future application of multivariate methods to data-rich studies should substantially enhance information recovery from epidemiological studies.
Publisher: Springer Science and Business Media LLC
Date: 15-04-2007
DOI: 10.1038/NG2026
Abstract: Characterizing the relationships between genomic and phenotypic variation is essential to understanding disease etiology. Information-dense data sets derived from pathophysiological, proteomic and transcriptomic profiling have been applied to map quantitative trait loci (QTLs). Metabolic traits, already used in QTL studies in plants, are essential phenotypes in mammalian genetics to define disease biomarkers. Using a complex mammalian system, here we show chromosomal mapping of untargeted plasma metabolic fingerprints derived from NMR spectroscopic analysis in a cross between diabetic and control rats. We propose candidate metabolites for the most significant QTLs. Metabolite profiling in congenic strains provided evidence of QTL replication. Linkage to a gut microbial metabolite (benzoate) can be explained by deletion of a uridine diphosphate glucuronosyltransferase. Mapping metabotypic QTLs provides a practical approach to understanding genome-phenotype relationships in mammals and may uncover deeper biological complexity, as extended genome (microbiome) perturbations that affect disease processes through transgenomic effects may influence QTL detection.
Publisher: Royal Society of Chemistry (RSC)
Date: 21-08-2002
DOI: 10.1039/B205128C
Abstract: 1H nuclear magnetic resonance (NMR)-based metabonomics is a well-established technique used to analyse and interpret complex multiparametric metabolic data, and has a wide number of applications in the development of pharmaceuticals. However, interpretation of biological data can be confounded by extraneous variation in the data such as fluctuations in either experimental conditions or in physiological status. Here we have shown the novel application of a data filtering method, orthogonal signal correction (OSC), to biofluid NMR data to minimise the influence of inter- and intra-spectrometer variation during data acquisition, and also to minimise innate physiological variation. The removal of orthogonal variation exposed features of interest in the NMR data and facilitated interpretation of the derived multivariate models. Furthermore, analysis of the orthogonal variation provided an explanation of the systematic analytical/biological changes responsible for confounding the original NMR data.
Publisher: Public Library of Science (PLoS)
Date: 27-02-2008
Publisher: Royal Society of Chemistry (RSC)
Date: 2010
DOI: 10.1039/B907021D
Abstract: The widely-used blood anticoagulants citrate and EDTA give rise to prominent peaks in (1)H NMR spectra of plasma s les collected in epidemiological and clinical studies, and these cause varying levels of interference in recovering biochemical information on endogenous metabolites. To investigate both the potential metabolic information loss caused by these substances and any possible inter-molecular interactions between the anticoagulants and endogenous components, the (1)H NMR spectra of 40 split human plasma s les collected from 20 in iduals into either citrate or EDTA have been analysed. Endogenous metabolite peaks were selectively obscured by large citrate peaks or those from free EDTA and its calcium and magnesium complexes. It is shown that the endogenous metabolites that give rise to peaks obscured by those from EDTA or citrate almost invariably also have other resonances that allow their identification and potential quantitation. Also, metabolic information recovery could be maximised by use of spectral editing techniques such as spin-echo, diffusion-editing and J-resolved experiments. The NMR spectral effects of any interactions between the added citrate or EDTA and endogenous components were found to be negligible. Finally, identification of split s les was feasible using simple multivariate statistical approaches such as principal components analysis. Thus even when legacy epidemiological plasma s les have been collected using the NMR-inappropriate citrate or EDTA anticoagulants, useful biochemical information can still be recovered effectively.
Publisher: American Chemical Society (ACS)
Date: 28-11-2011
DOI: 10.1021/AC202516E
Publisher: Wiley
Date: 11-2010
DOI: 10.1002/CEM.1359
Abstract: Metabonomics is a key element in systems biology, and with current analytical methods, generates vast amounts of quantitative or qualitative metabolic data. Understanding of the global function of the living organism can be achieved by integration of ‘omics’ approaches including metabonomics, genomics, transcriptomics and proteomics, increasing the complexity of the full data sets. Multivariate statistical approaches are well suited to extract the characterizing metabolic information associated with each level of dynamic process. In this review, we discuss techniques that have evolved from principal component analysis and partial least squares (PLS) methods with a focus on improved interpretation and modeling with respect to biomarker recovery and data visualization in the context of metabonomic applications. Visualization is of paramount importance to investigate complex metabolic signatures, the power and potential of which is illustrated with key papers. Recent improvements based on the removal of orthogonal variation are discussed in terms of interpretation enhancement, and are supported by relevant applications. Flexibility of PLS methods in general and of O‐PLS in particular allows implementation of derivative methods such as O2‐PLS, O‐PLS‐variance components, nonlinear methods, and batch modeling to improve analysis of complex data sets, which facilitates extraction of information related to subtle biological processes. These approaches can be used to address issues present in complex multi‐factorial data sets. Thus, we highlight the key advantages and limitations of the different latent variable applications for top‐down systems biology and assess the differences between the methods available. Copyright © 2010 John Wiley & Sons, Ltd.
Publisher: American Chemical Society (ACS)
Date: 07-01-2011
DOI: 10.1021/PR1003278
Abstract: Surgical trauma initiates a complex series of metabolic host responses designed to maintain homeostasis and ensure survival. (1)H NMR spectroscopy was applied to intraoperative urine and plasma s les as part of a strategy to analyze the metabolic response of Wistar rats to a laparotomy model. Spectral data were analyzed by multivariate statistical analysis. Principal component analysis (PCA) confirmed that surgical injury is responsible for the majority of the metabolic variability demonstrated between animals (R² Urine = 81.2% R² plasma = 80%). Further statistical analysis by orthogonal projection to latent structure discriminant analysis (OPLS-DA) allowed the identification of novel urinary metabolic markers of surgical trauma. Urinary levels of taurine, glucose, urea, creatine, allantoin, and trimethylamine-N-oxide (TMAO) were significantly increased after surgery whereas citrate and 2-oxoglutarate (2-OG) negatively correlated with the intraoperative state as did plasma levels of betaine and tyrosine. Plasma levels of lipoproteins such as VLDL and LDL also rose with the duration of surgery. Moreover, the microbial cometabolites 3-hydroxyphenylpropionate, phenylacetylglycine, and hippurate correlated with the surgical insult, indicating that the gut microbiota are highly sensitive to the global homeostatic state of the host. Metabonomic profiling provides a global overview of surgical trauma that has the potential to provide novel biomarkers for personalized surgical optimization and outcome prediction.
Publisher: American Chemical Society (ACS)
Date: 05-07-2011
DOI: 10.1021/AC201065J
Abstract: Ultra-performance liquid chromatography coupled to mass spectrometry (UPLC/MS) has been used increasingly for measuring changes of low molecular weight metabolites in biofluids/tissues in response to biological challenges such as drug toxicity and disease processes. Typically s les show high variability in concentration, and the derived metabolic profiles have a heteroscedastic noise structure characterized by increasing variance as a function of increased signal intensity. These sources of experimental and instrumental noise substantially complicate information recovery when statistical tools are used. We apply and compare several preprocessing procedures and introduce a statistical error model to account for these bioanalytical complexities. In particular, the use of total intensity, median fold change, locally weighted scatter plot smoothing, and quantile normalizations to reduce extraneous variance induced by s le dilution were compared. We demonstrate that the UPLC/MS peak intensities of urine s les should respond linearly to variable s le dilution across the intensity range. While all four studied normalization methods performed reasonably well in reducing dilution-induced variation of urine s les in the absence of biological variation, the median fold change normalization is least compromised by the biologically relevant changes in mixture components and is thus preferable. Additionally, the application of a subsequent log-based transformation was successful in stabilizing the variance with respect to peak intensity, confirming the predominant influence of multiplicative noise in peak intensities from UPLC/MS-derived metabolic profile data sets. We demonstrate that variance-stabilizing transformation and normalization are critical preprocessing steps that can benefit greatly metabolic information recovery from such data sets when widely applied chemometric methods are used.
Publisher: Elsevier BV
Date: 07-2017
Publisher: American Chemical Society (ACS)
Date: 25-01-2005
DOI: 10.1021/AC048630X
Abstract: We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole s le. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.
Publisher: American Physiological Society
Date: 12-2006
DOI: 10.1152/PHYSIOLGENOMICS.00084.2006
Abstract: Caloric restriction (CR) increases healthy life span in a range of organisms. The underlying mechanisms are not understood but appear to include changes in gene expression, protein function, and metabolism. Recent studies demonstrate that acute CR alters mortality rates within days in flies. Multitissue transcriptional changes and concomitant metabolic responses to acute CR have not been described. We generated whole genome RNA transcript profiles in liver, skeletal muscle, colon, and hypothalamus and simultaneously measured plasma metabolites using proton nuclear magnetic resonance in mice subjected to acute CR. Liver and muscle showed increased gene expressions associated with fatty acid metabolism and a reduction in those involved in hepatic lipid biosynthesis. Glucogenic amino acids increased in plasma, and gene expression for hepatic gluconeogenesis was enhanced. Increased expression of genes for hormone-mediated signaling and decreased expression of genes involved in protein binding and development occurred in hypothalamus. Cell proliferation genes were decreased and cellular transport genes increased in colon. Acute CR captured many, but not all, hepatic transcriptional changes of long-term CR. Our findings demonstrate a clear transcriptional response across multiple tissues during acute CR, with congruent plasma metabolite changes. Liver and muscle switched gene expression away from energetically expensive biosynthetic processes toward energy conservation and utilization processes, including fatty acid metabolism and gluconeogenesis. Both muscle and colon switched gene expression away from cellular proliferation. Mice undergoing acute CR rapidly adopt many transcriptional and metabolic changes of long-term CR, suggesting that the beneficial effects of CR may require only a short-term reduction in caloric intake.
Publisher: Proceedings of the National Academy of Sciences
Date: 15-08-2006
Abstract: Here, we study the intricate relationship between gut microbiota and host cometabolic phenotypes associated with dietary-induced impaired glucose homeostasis and nonalcoholic fatty liver disease (NAFLD) in a mouse strain (129S6) known to be susceptible to these disease traits, using plasma and urine metabotyping, achieved by 1 H NMR spectroscopy. Multivariate statistical modeling of the spectra shows that the genetic predisposition of the 129S6 mouse to impaired glucose homeostasis and NAFLD is associated with disruptions of choline metabolism, i.e., low circulating levels of plasma phosphatidylcholine and high urinary excretion of methylamines (dimethylamine, trimethylamine, and trimethylamine- N -oxide), coprocessed by symbiotic gut microbiota and mammalian enzyme systems. Conversion of choline into methylamines by microbiota in strain 129S6 on a high-fat diet reduces the bioavailability of choline and mimics the effect of choline-deficient diets, causing NAFLD. These data also indicate that gut microbiota may play an active role in the development of insulin resistance.
Publisher: American Chemical Society (ACS)
Date: 13-12-2004
DOI: 10.1021/AC048803I
Abstract: In general, applications of metabonomics using biofluid NMR spectroscopic analysis for probing abnormal biochemical profiles in disease or due to toxicity have all relied on the use of chemometric techniques for s le classification. However, the well-known variability of some chemical shifts in 1H NMR spectra of biofluids due to environmental differences such as pH variation, when coupled with the large number of variables in such spectra, has led to the situation where it is necessary to reduce the size of the spectra or to attempt to align the shifting peaks, to get more robust and interpretable chemometric models. Here, a new approach that avoids this problem is demonstrated and shows that, moreover, inclusion of variable peak position data can be beneficial and can lead to useful biochemical information. The interpretation of chemometric models using combined back-scaled loading plots and variable weights demonstrates that this peak position variation can be handled successfully and also often provides additional information on the physicochemical variations in metabonomic data sets.
Publisher: American Chemical Society (ACS)
Date: 03-05-2007
DOI: 10.1021/PR060412S
Abstract: The biochemical effects of acute and chronic psychological stress have been investigated in male Sprague-Dawley rats using a combination of 1H NMR spectral analysis of plasma and conventional hematological analyses. Animals were subjected to 35 consecutive days of 6-h sessions of stress, and following a 9 day break, were stressed for a further 6-h period. Plasma s les were collected at 0, 1, 3, and 6 h on days 1, 9, 21, 35, and 44, measured using 600 MHz 1H NMR spectroscopy, and analyzed by Principal Components Analysis. Time-dependent biochemical effects of psychological stress on a range of endogenous metabolites were evident and were correlated with the intensity of the stress response as defined by corticosterone and hematological parameters. Following acute stress, increases in the levels of glucose and ketone bodies, and decreases in the levels of acetate, alanine, isoleucine, lactate, leucine, valine, and lipoproteins, were observed. Chronic stress-induced increases in plasma levels of alanine, lactate (day 9), and leucine, valine, and choline (day 44) and decreases in acetate (day 9) and lipoprotein concentrations were observed. Positive correlations between plasma corticosterone level and glucose and glycerol, and between plasma lipoprotein concentrations and hemoglobin levels, were established using Projection to Latent Structures (PLS) analysis. This study indicates the potential of using NMR-based metabonomic strategies for the characterization of endogenous metabolic perturbations induced by psychological stressors and lifestyle choices.
Publisher: Springer Science and Business Media LLC
Date: 07-05-2021
DOI: 10.1186/S40168-021-01052-7
Abstract: The gut microbiome and iron status are known to play a role in the pathophysiology of non-alcoholic fatty liver disease (NAFLD), although their complex interaction remains unclear. Here, we applied an integrative systems medicine approach (faecal metagenomics, plasma and urine metabolomics, hepatic transcriptomics) in 2 well-characterised human cohorts of subjects with obesity (discovery n = 49 and validation n = 628) and an independent cohort formed by both in iduals with and without obesity ( n = 130), combined with in vitro and animal models. Serum ferritin levels, as a markers of liver iron stores, were positively associated with liver fat accumulation in parallel with lower gut microbial gene richness, composition and functionality. Specifically, ferritin had strong negative associations with the Pasteurellaceae , Leuconostocaceae and Micrococcaea families. It also had consistent negative associations with several Veillonella , Bifidobacterium and Lactobacillus species, but positive associations with Bacteroides and Prevotella spp. Notably, the ferritin-associated bacterial families had a strong correlation with iron-related liver genes. In addition, several bacterial functions related to iron metabolism (transport, chelation, heme and siderophore biosynthesis) and NAFLD (fatty acid and glutathione biosynthesis) were also associated with the host serum ferritin levels. This iron-related microbiome signature was linked to a transcriptomic and metabolomic signature associated to the degree of liver fat accumulation through hepatic glucose metabolism. In particular, we found a consistent association among serum ferritin, Pasteurellaceae and Micrococcacea families, bacterial functions involved in histidine transport, the host circulating histidine levels and the liver expression of GYS2 and SEC24B. Serum ferritin was also related to bacterial glycine transporters, the host glycine serum levels and the liver expression of glycine transporters. The transcriptomic findings were replicated in human primary hepatocytes, where iron supplementation also led to triglycerides accumulation and induced the expression of lipid and iron metabolism genes in synergy with palmitic acid. We further explored the direct impact of the microbiome on iron metabolism and liver fact accumulation through transplantation of faecal microbiota into recipient’s mice. In line with the results in humans, transplantation from ‘high ferritin donors’ resulted in alterations in several genes related to iron metabolism and fatty acid accumulation in recipient’s mice. Altogether, a significant interplay among the gut microbiome, iron status and liver fat accumulation is revealed, with potential significance for target therapies.
Publisher: Springer Science and Business Media LLC
Date: 06-07-2007
DOI: 10.1007/S00125-007-0738-5
Abstract: Complex changes in gene expression are associated with insulin resistance and non-alcoholic fatty liver disease (NAFLD) promoted by feeding a high-fat diet (HFD). We used functional genomic technologies to document molecular mechanisms associated with diet-induced NAFLD. Male 129S6 mice were fed a diet containing 40% fat (high-fat diet, HFD) for 15 weeks. Glucose tolerance, in vivo insulin secretion, plasma lipid profile and adiposity were determined. Plasma metabonomics and liver transcriptomics were used to identify changes in gene expression associated with HFD-induced NAFLD. In HFD-fed mice, NAFLD and impaired glucose and lipid homeostasis were associated with increased hepatic transcription of genes involved in fatty acid uptake, intracellular transport, modification and elongation, whilst genes involved in beta-oxidation and lipoprotein secretion were, paradoxically, also upregulated. NAFLD developed despite strong and sustained downregulation of transcription of the gene encoding stearoyl-coenzyme A desaturase 1 (Scd1) and uncoordinated regulation of transcription of Scd1 and the gene encoding sterol regulatory element binding factor 1c (Srebf1c) transcription. Inflammatory mechanisms appeared to be stimulated by HFD. Our results provide an accurate representation of subtle changes in metabolic and gene expression regulation underlying disease-promoting and compensatory mechanisms, collectively contributing to diet-induced insulin resistance and NAFLD. They suggest that proposed models of NAFLD pathogenesis can be enriched with novel diet-reactive genes and disease mechanisms.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Richard H. Barton.