Richard H. Barton

Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice

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 1H 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.

Optimized Preprocessing of Ultra-Performance Liquid Chromatography/Mass Spectrometry Urinary Metabolic Profiles for Improved Information Recovery

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 samples 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 sample dilution were compared. We demonstrate that the UPLC/MS peak intensities of urine samples should respond linearly to variable sample dilution across the intensity range. While all four studied normalization methods performed reasonably well in reducing dilution-induced variation of urine samples 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.

Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models

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.

The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping

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

High-throughput 1H NMR-based metabolic analysis of human serum and urine for large-scale epidemiological studies: validation study

BACKGROUND 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. METHODS 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 samples 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. RESULTS 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. CONCLUSIONS 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.

Phylometabonomic Patterns of Adaptation to High Fat Diet Feeding in Inbred Mice

Insulin resistance plays a central role in type 2 diabetes and obesity, which develop as a consequence of genetic and environmental factors. Dietary changes including high fat diet (HFD) feeding promotes insulin resistance in rodent models which present useful systems for studying interactions between genetic background and environmental influences contributing to disease susceptibility and progression. We applied a combination of classical physiological, biochemical and hormonal studies and plasma 1H NMR spectroscopy-based metabonomics to characterize the phenotypic and metabotypic consequences of HFD (40%) feeding in inbred mouse strains (C57BL/6, 129S6, BALB/c, DBA/2, C3H) frequently used in genetic studies. We showed the wide range of phenotypic and metabonomic adaptations to HFD across the five strains and the increased nutrigenomic predisposition of 129S6 and C57BL/6 to insulin resistance and obesity relative to the other strains. In contrast mice of the BALB/c and DBA/2 strains showed relative resistance to HFD-induced glucose intolerance and obesity. Hierarchical metabonomic clustering derived from 1H NMR spectral data of the strains provided a phylometabonomic classification of strain-specific metabolic features and differential responses to HFD which closely match SNP-based phylogenetic relationships between strains. Our results support the concept of genomic clustering of functionally related genes and provide important information for defining biological markers predicting spontaneous susceptibility to insulin resistance and pathological adaptations to fat feeding.

Application of orthogonal signal correction to minimise the effects of physical and biological variation in high resolution 1H NMR spectra of biofluids

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.

Global metabolic phenotyping in an experimental laparotomy model of surgical trauma.

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 samples 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.

The influence of EDTA and citrate anticoagulant addition to human plasma on information recovery from NMR-based metabolic profiling studies.

The widely-used blood anticoagulants citrate and EDTA give rise to prominent peaks in (1)H NMR spectra of plasma samples 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 samples collected from 20 individuals 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 samples was feasible using simple multivariate statistical approaches such as principal components analysis. Thus even when legacy epidemiological plasma samples have been collected using the NMR-inappropriate citrate or EDTA anticoagulants, useful biochemical information can still be recovered effectively.

The analysis of para-cresol production and tolerance in Clostridium difficile 027 and 012 strains

BackgroundClostridium difficile is the major cause of antibiotic associated diarrhoea and in recent years its increased prevalence has been linked to the emergence of hypervirulent clones such as the PCR-ribotype 027. Characteristically, C. difficile infection (CDI) occurs after treatment with broad-spectrum antibiotics, which disrupt the normal gut microflora and allow C. difficile to flourish. One of the relatively unique features of C. difficile is its ability to ferment tyrosine to para-cresol via the intermediate para-hydroxyphenylacetate (p-HPA). P-cresol is a phenolic compound with bacteriostatic properties which C. difficile can tolerate and may provide the organism with a competitive advantage over other gut microflora, enabling it to proliferate and cause CDI. It has been proposed that the hpdBCA operon, rarely found in other gut microflora, encodes the enzymes responsible for the conversion of p-HPA to p-cresol.ResultsWe show that the PCR-ribotype 027 strain R20291 quantitatively produced more p-cresol in-vitro and was significantly more tolerant to p-cresol than the sequenced strain 630 (PCR-ribotype 012). Tyrosine conversion to p-HPA was only observed under certain conditions. We constructed gene inactivation mutants in the hpdBCA operon in strains R20291 and 630Δerm which curtails their ability to produce p-cresol, confirming the role of these genes in p-cresol production. The mutants were equally able to tolerate p-cresol compared to the respective parent strains, suggesting that tolerance to p-cresol is not linked to its production.ConclusionsC. difficile converts tyrosine to p-cresol, utilising the hpdBCA operon in C. difficile strains 630 and R20291. The hypervirulent strain R20291 exhibits increased production of and tolerance to p-cresol, which may be a contributory factor to the virulence of this strain and other hypervirulent PCR-ribotype 027 strains.