12 research outputs found

    MetaboNetworks, an interactive Matlab-based toolbox for creating, customizing and exploring sub-networks from KEGG.

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    Summary: MetaboNetworks is a tool to create custom sub-networks in Matlab using main reaction pairs as defined by the Kyoto Encyclopaedia of Genes and Genomes and can be used to explore transgenomic interactions, for example mammalian and bacterial associations. It calculates the shortest path between a set of metabolites (e.g. biomarkers from a metabonomic study) and plots the connectivity between metabolites as links in a network graph. The resulting graph can be edited and explored interactively. Furthermore, nodes and edges in the graph are linked to the Kyoto Encyclopaedia of Genes and Genomes compound and reaction pair web pages. Availability and implementation: MetaboNetworks is available from http://www.mathworks.com/matlabcentral/fileexchange/42684. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

    Metabolic phenotyping for understanding the gut microbiome and host metabolic interplay

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    There is growing interest in the role of the gut microbiome in human health and disease. This unique complex ecosystem has been implicated in a number of health conditions including intestinal disorders, inflammatory skin diseases and metabolic syndrome. However, there is still much to learn regarding its capacity to affect host health. Many gut microbiome research studies focus on compositional analysis to better understand the causal relationships between microbial communities and disease phenotypes. Yet microbial diversity and complexity is such, that community structure alone does not provide full understanding of microbial function. Metabolic phenotyping is an exciting field in systems biology that provides information on metabolic outputs taking place in the system at a given moment in time. These readouts provide information relating to by-products of endogenous metabolic pathways, exogenous signals arising from diet, drugs and other lifestyle and environmental stimuli, as well as products of microbe-host co-metabolism. Thus, better understanding of the gut microbiome and host metabolic interplay can be gleaned by using such analytical approaches. In this Review, we describe research findings focussed on gut microbiota-host interactions, for functional insight into the impact of microbiome composition on host health. We evaluate different analytical approaches for capturing metabolic activity, and discuss analytical methodological advancements that have made a contribution to the field. This information will aid in developing novel approaches to improve host health in the future, and therapeutic modulation of the microbiome may soon augment conventional clinical strategies

    Novel statistical and bioinformatic tools for identifying predictive metabolic biomarkers in molecular epidemiology studies

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    A top-down systems biology approach investigating metabolic responses to external stimuli or physiological processes requires multivariate statistical tools to identify metabolites associated with the global biochemical changes in a supra-organism. In this thesis I describe several tools I have developed to improve or supplement currently used methods in molecular epidemiology studies. First, I describe the MetaboNetworks toolbox which is able to create custom, multi-compartmental metabolic reaction networks for a supra-organism, combining both mammalian and microbial reactions. These networks are essentially a summary of the supra-organisms homeostatic signature. Second, I describe a novel statistical spectroscopy approach called STORM which aids in the elucidation of unknown biomarker signals in 1H NMR spectra. Third, I describe the Metabolome-Wide Association Study on obesity in U.S. and U.K. populations. Many novel metabolic associations with obesity are described in a systems framework, among which metabolites associated with energy, skeletal muscle, lipid, amino acid and gut microbial metabolism. Last, I describe a new multivariate approach to adjust for confounders, CA-OPLS. Correcting for confounders is an essential aspect in molecular epidemiology studies as metabolites can be related to a variety of factors such as lifestyle, diet and environmental exposures which or may not be causally related to disease risk. In developing CA-OPLS another aim was to simultaneously eliminate/minimize the effects of different types of sampling bias which are often not taken into account in modelling metabonomics data with current methods.Open Acces

    Genetic mapping of metabolic biomarkers of cardiometabolic diseases

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    Cardiometabolic disorders (CMDs) are a major public health problem worldwide. The main goal of this thesis is to characterize the genetic architecture of CMD-related metabolites in a Lebanese cohort. In order to maximise the extraction of meaningful biological information from this dataset, an important part of this thesis focuses on the evaluation and subsequent improvement of the standard methods currently used for molecular epidemiology studies. First, I describe MetaboSignal, a novel network-based approach to explore the genetic regulation of the metabolome. Second, I comprehensively compare the recovery of metabolic information in the different 1H NMR strategies routinely used for metabolic profiling of plasma (standard 1D, spin-echo and JRES). Third, I describe a new method for dimensionality reduction of 1H NMR datasets prior to statistical modelling. Finally, I use all this methodological knowledge to search for molecular biomarkers of CMDs in a Lebanese population. Metabolome-wide association analyses identified a number of metabolites associated with CMDs, as well as several associations involving N-glycan units from acute-phase glycoproteins. Genetic mapping of these metabolites validated previously reported gene-metabolite associations, and revealed two novel loci associated with CMD-related metabolites. Collectively, this work contributes to the ongoing efforts to characterize the molecular mechanisms underlying complex human diseases.Open Acces

    Topological Analysis of Metabolic Networks Integrating Co-Segregating Transcriptomes and Metabolomes in Type 2 Diabetic Rat Congenic Series

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    Background: The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus is caused by complex organ-specific cellular mechanisms contributing to impaired insulin secretion and insulin resistance. Methods: We used systematic metabotyping by 1H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualise shortest paths between metabolites and genes significantly associated with each genomic block. Results: Despite strong genomic similarities (95-99%) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific metabotypes (mQTL) and genome-wide expression traits (eQTL). Variation in key metabolites like glucose, succinate, lactate or 3-hydroxybutyrate, and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing shortest path length drove prioritization of biological validations by gene silencing. Conclusions: These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulations and to characterize novel functional roles for genes determining tissue-specific metabolism

    Protocol for faecal microbiota transplantation in ulcerative colitis (FMTUC): a randomised feasibility study

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    Background The interaction of the gut microbiota with the human host is implicated in the pathogenesis of inflammatory and immunological diseases including ulcerative colitis (UC). Faecal microbiota transplantation (FMT) as a method of restoring gut microbial diversity is of increasing interest as a therapeutic approach in the management of UC. The current literature lacks consensus about the dose of FMT, route of administration and duration of response. Methods and analysis This single-blinded randomised trial will explore the feasibility of FMT in 30 treatment-naïve patients with histologically confirmed distal UC limited to the recto-sigmoid region (up to 40 cm from the anal verge). This study aims to estimate the magnitude of treatment response to FMT under controlled conditions. The intervention (FMT) will be administered by rectal retention enema. It will test the feasibility of randomising patients to: (i) single FMT dose, (ii) five daily FMT doses or (iii) control (no FMT dose). All groups will receive standard antibiotic gut decontamination and bowel preparation before FMT. Recruitment will take place over a 24-month period with a 12-week patient follow-up. Trial objectives include evaluation of the magnitude of treatment response to FMT, investigation of the clinical value of metabolic phenotyping for predicting the clinical response to FMT and testing the recruitment rate of donors and patients for a study in FMT. This feasibility trial will enable an estimate of number of patients needed, help determine optimal study conditions and inform the choice of endpoints for a future definitive phase III study. Ethics and dissemination The trial is approved by the regional ethics committee and is sponsored by Abertawe Bro Morgannwg University’s Health Board. Written informed consent from all patients will be obtained. Serious adverse events will be reported to the sponsor. Trial results will be disseminated via peer review publication and shared with trial participants. Trial registration number ISRCTN58082603; Pre-results

    The gut microbiota influences skeletal muscle mass and function in mice

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    The functional interactions between the gut microbiota and the host are important for host physiology, homeostasis, and sustained health. We compared the skeletal muscle of germ-free mice that lacked a gut microbiota to the skeletal muscle of pathogen-free mice that had a gut microbiota. Compared to pathogen-free mouse skeletal muscle, germ-free mouse skeletal muscle showed atrophy, decreased expression of insulin-like growth factor 1, and reduced transcription of genes associated with skeletal muscle growth and mitochondrial function. Nuclear magnetic resonance spectrometry analysis of skeletal muscle, liver, and serum from germ-free mice revealed multiple changes in the amounts of amino acids, including glycine and alanine, compared to pathogen-free mice. Germ-free mice also showed reduced serum choline, the precursor of acetylcholine, the key neurotransmitter that signals between muscle and nerve at neuromuscular junctions. Reduced expression of genes encoding Rapsyn and Lrp4, two proteins important for neuromuscular junction assembly and function, was also observed in skeletal muscle from germ-free mice compared to pathogen-free mice. Transplanting the gut microbiota from pathogen-free mice into germ-free mice resulted in an increase in skeletal muscle mass, a reduction in muscle atrophy markers, improved oxidative metabolic capacity of the muscle, and elevated expression of the neuromuscular junction assembly genes Rapsyn and Lrp4 Treating germ-free mice with short-chain fatty acids (microbial metabolites) partly reversed skeletal muscle impairments. Our results suggest a role for the gut microbiota in regulating skeletal muscle mass and function in mice

    Investigating the gut-lung axis metabolic profiling and the effect of SCFAs on airway epithelium in healthy and asthmatics

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    Asthma is a chronic inflammatory disease characterized by decline in lung function, goblet cell metaplasia and tissue remodeling. Recent years have demonstrated the interplay between the airways and the gut microbiota, showcasing the dampening of airway inflammation by gut-microbial metabolites short-chain fatty acids (SCFAs) in rodent models. No studies have investigated the presence and effect of SCFAs in the airways of asthmatics and limited studies have investigated the metabolic profile of asthmatics and the effect of fibre supplementation. We demonstrate the presence of SCFAs (mainly acetate and propionate) in the airways of healthy controls and asthmatics, with an increase in acetate in asthmatics. The levels of SCFAs detected in the airways exceeded what has previously been described in peripheral blood. SCFA concentrations in asthmatic airway were correlated with specific airway microbial genera, suggesting a possible production of SCFAs by the airway microbiota. This was further confirmed by in vitro batch culture, where airway bacterial species produced higher amount of SCFAs in the presence of mucin, indicating proteolytic activity by the airway microbiota. In order to determine the direct effect, which local SCFA level could have on the airway epithelium, the effect of acetate and propionate were investigated on primary airway epithelium. Both SCFAs promoted a modulation of cytokine gene response upon, stimulation on both apical and basolateral side, which was further enhanced by IL-13 treatment. These results suggest that the microbial-airway-derived SCFAs promote a local change in the inflammatory response in vitro, which is different to the response seen with gut-microbial-derived SCFAs in preclinical data. Furthermore, the metabolic profile of asthmatics was investigate using 1H NMR spectroscopy and UPLC-MS, in order to identify potential urinary biomarkers that would aid in the diagnosis of asthma and sub-populations of asthma. Several urinary biomarkers were found to be either elevated or decreased in asthmatics belonging to purine, polyamine and fatty acid metabolism. Interestingly, a panel of carnitines were able to distinguish between sub-population of severe asthmatics. Lastly, investigating the effect of prebiotic supplementation to individuals with exercise-induced asthma, did not generate any statistically significant effect on SCFAs concentrations nor lung function, possibly due to the low levels of prebiotic supplementation.Open Acces
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