987 results found
McArthur S, Umlai UK, Snelling T, et al., 2017, Effects of gut-derived methylamines on the blood–brain barrier, 2017 Alzheimer's Research UK Conference
Introduction: Composition and functions of the gut microbiota are inextricably linked with host health, and altered in conditions such as obesity and type II diabetes. Central to microbe–host crosstalk are gut-derived microbial metabolites, of which trimethylamine N-oxide (TMAO) and its precursor trimethylamine (TMA) are of particular importance. TMA produced by intestinal microbes is converted to TMAO in the liver by flavin monooxygenases with circulating TMAO being associated with cardiovascular disease and insulin resistance. TMAO was also recently identified as potentially important in genetic pathways associated with Alzheimer’s disease (AD). In considering that deficits in blood–brain barrier (BBB) function occur early in AD, and its position as the major interface between circulating metabolites and the brain, we investigated the effects of TMAO and TMA on key BBB properties in vitro.Materials and Methods: Human hCMEC/D3 cerebromicrovascular cells were used as an in vitro model of the BBB to investigate the effects of 24 h treatment with physiologically relevant doses of TMAO and TMA, studying (i) functional barrier properties of cell monolayers, (ii) Aβ efflux transporters and (iii) gene expression.Results: Exposure of hCMEC/D3 cells to TMAO (40 μM) reinforced barrier integrity by enhancing transendothelial electrical resistance (P <0.001) and reducing paracellular permeability to a 70 kDa dextran tracer (P <0.001). In contrast, while TMA (0.4 μM) enhanced electrical resistance (P <0.001), it significantly increased tracer paracellular permeability (P <0.05), consistent with compromised barrier function. Transporter activity analysis showed TMAO inhibited p-glycoprotein function (P <0.001), which was not seen with TMA; neither metabolite affected BCRP function. Human-genome transcriptomic data are currently being analysed.Conclusions: TMAO and TMA affect BBB function in a metabolite-specific manner, regulating barr
Hoyles L, Fernández-Real JM, Federici M, et al., 2017, Integrated systems biology to study non-alcoholic fatty liver disease in obese women, Gut Microbiota for Health World Summit 2017
Objectives: To integrate metagenomic (faecal microbiome), transcriptomic, metabonomic and clinical data to evaluate the contribution of the gut microbiome to the molecular phenome (hepatic transcriptome, plasma and urine metabonome) of non-alcoholic fatty liver disease (NAFLD) independent of clinical confounders in morbidly obese women recruited to the FLORINASH study.Methods: Faecal, liver biopsy, blood and urine samples and data for 28 clinical variables were collected for 56 obese [body mass index (BMI) >35] women from Italy (n = 31) and Spain (n = 25) who elected for bariatric surgery. Confounder analyses of clinical data were done using linear modeling. Histological examination of liver biopsies was used to grade NAFLD (NAFLD activity score: 0, 1, 2, 3). Faecal metagenomes were generated and analysed using the Imperial Metagenomics Pipeline. Differentially expressed genes were identified in hepatic transcriptomes, and analysed using Enrichr, network analyses and Signaling Pathway Impact Analysis. 1H-NMR data were generated for plasma and urinary metabonomes. Clinical, metagenomic, transcriptomic and metabonomic data were integrated using partial Spearman’s correlation, taking confounders (age, body mass index and cohort) into account.Results: NAFLD activity score was anti-correlated with microbial gene richness, and correlated with abundance of Proteobacteria. KEGG analyses of metagenomic data suggested increased microbial processing of dietary lipids and amino acids, as well as endotoxin-related processes related to Proteobacteria. Metabonomic profiles highlighted imbalances in choline metabolism, branched-chain amino acid metabolism and gut-derived microbial metabolites resulting from metabolism of amino acids. NAFLD-associated hepatic transcriptomes were associated with branched-chain amino acid metabolism, endoplasmic reticulum/phagosome, and immune responses associated with microbial infections. Molecular phenomic signatures were stable and predic
Alexander JL, Wilson ID, Teare J, et al., 2017, Gut microbiota modulation of chemotherapy efficacy and toxicity., Nat Rev Gastroenterol Hepatol, Vol: 14, Pages: 356-365
Evidence is growing that the gut microbiota modulates the host response to chemotherapeutic drugs, with three main clinical outcomes: facilitation of drug efficacy; abrogation and compromise of anticancer effects; and mediation of toxicity. The implication is that gut microbiota are critical to the development of personalized cancer treatment strategies and, therefore, a greater insight into prokaryotic co-metabolism of chemotherapeutic drugs is now required. This thinking is based on evidence from human, animal and in vitro studies that gut bacteria are intimately linked to the pharmacological effects of chemotherapies (5-fluorouracil, cyclophosphamide, irinotecan, oxaliplatin, gemcitabine, methotrexate) and novel targeted immunotherapies such as anti-PD-L1 and anti-CLTA-4 therapies. The gut microbiota modulate these agents through key mechanisms, structured as the 'TIMER' mechanistic framework: Translocation, Immunomodulation, Metabolism, Enzymatic degradation, and Reduced diversity and ecological variation. The gut microbiota can now, therefore, be targeted to improve efficacy and reduce the toxicity of current chemotherapy agents. In this Review, we outline the implications of pharmacomicrobiomics in cancer therapeutics and define how the microbiota might be modified in clinical practice to improve efficacy and reduce the toxic burden of these compounds.
Chekmeneva E, Correia GDS, Chan Q, et al., 2017, Optimization and Application of Direct Infusion Nanoelectrospray HRMS Method for Large-Scale Urinary Metabolic Phenotyping in Molecular Epidemiology, JOURNAL OF PROTEOME RESEARCH, Vol: 16, Pages: 1646-1658, ISSN: 1535-3893
Large-scale metabolic profiling requires the development of novel economical high-throughput analytical methods to facilitate characterization of systemic metabolic variation in population phenotypes. We report a fit-for-purpose direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) method with time-of-flight detection for rapid targeted parallel analysis of over 40 urinary metabolites. The newly developed 2 min infusion method requires <10 μL of urine sample and generates high-resolution MS profiles in both positive and negative polarities, enabling further data mining and relative quantification of hundreds of metabolites. Here we present optimization of the DI-nESI-HRMS method in a detailed step-by-step guide and provide a workflow with rigorous quality assessment for large-scale studies. We demonstrate for the first time the application of the method for urinary metabolic profiling in human epidemiological investigations. Implementation of the presented DI-nESI-HRMS method enabled cost-efficient analysis of >10 000 24 h urine samples from the INTERMAP study in 12 weeks and >2200 spot urine samples from the ARIC study in <3 weeks with the required sensitivity and accuracy. We illustrate the application of the technique by characterizing the differences in metabolic phenotypes of the USA and Japanese population from the INTERMAP study.
Posma JM, Garcia Perez I, Heaton JC, et al., 2017, An integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: application to dietary biomarkers, Analytical Chemistry, Vol: 89, Pages: 3300-3309, ISSN: 1086-4377
A major purpose of exploratory metabolic profiling is for the identification of molecular species that are statistically associated with specific biological or medical outcomes; unfortunately the structure elucidation process of unknowns is often a major bottleneck in this process. We present here new holistic strategies that combine different statistical spectroscopic and analytical techniques to improve and simplify the process of metabolite identification. We exemplify these strategies using study data collected as part of a dietary intervention to improve health and which elicits a relatively subtle suite of changes from complex molecular profiles. We identify three new dietary biomarkers related to the consumption of peas (N-methyl nicotinic acid), apples (rhamnitol) and onions (N-acetyl-S-(1Z)-propenyl-cysteine-sulfoxide) that can be used to enhance dietary assessment and assess adherence to diet. As part of the strategy, we introduce a new probabilistic statistical spectroscopy tool, RED-STORM (Resolution EnhanceD SubseT Optimization by Reference Matching), that uses 2D J-resolved ¹H-NMR spectra for enhanced information recovery using the Bayesian paradigm to extract a subset of spectra with similar spectral signatures to a reference. RED-STORM provided new information for subsequent experiments (e.g. 2D-NMR spectroscopy, Solid-Phase Extraction, Liquid Chromatography prefaced Mass Spectrometry) used to ultimately identify an unknown compound. In summary, we illustrate the benefit of acquiring J-resolved experiments alongside conventional 1D ¹H-NMR as part of routine metabolic profiling in large datasets and show that application of complementary statistical and analytical techniques for the identification of unknown metabolites can be used to save valuable time and resource.
Inglese P, McKenzie JS, Mroz A, et al., 2017, Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer, Chemical Science, Vol: 8, Pages: 3500-3511, ISSN: 2041-6539
Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.
Hoyles L, Fernández-Real JM, Federici M, et al., 2017, Integrated systems biology to study non-alcoholic fatty liver disease in obese women, MRC-PHE Centre for Environment & Health - Centre Training Programme Annual Meeting
Gray N, Zia R, King A, et al., 2017, High speed quantitative UPLC-MS analysis of multiple amines in human plasma and serum via pre-column derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate: Application to acetaminophen-induced liver failure, Analytical Chemistry, Vol: 89, Pages: 2478-2487, ISSN: 1520-6882
A targeted reversed-phase gradient UPLC-MS/MS assay has been developed for the quantification/monitoring of amino acids and amino-containing compounds in human plasma and serum using pre-column derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AccQTag UltraTM). Derivatization of the target amino-containing compounds reagent required minimal sample preparation and resulted in analytes with excellent chromatographic and mass spectrometric properties. The resulting method, which requires only 10 µl of sample, provides the reproducible and robust separation of 66 analytes in 7.5 minutes, including baseline resolution of isomers such as e.g. leucine and isoleucine. The assay has been validated for the quantification of 33 amino compounds (predominantly amino acids) over a concentration range from 2-20 and 800µM. Intra- and inter-day accuracy of between 0.05-15.6 and 0.78 -13.7 % and precision between 0.91-16.9 % and 2.12-15.9 % were obtained. A further 33 biogenic amines can be monitored in samples for relative changes in concentration rather than quantification. Application of the assay to samples derived from healthy controls and patients suffering from acetaminophen (APAP, paracetamol) induced acute liver failure (ALF) showed significant differences in the amounts of aromatic and branched chain amino acids between the groups as well as a number of other analytes, including the novel observation of increased concentrations of sarcosine in ALF patients. The properties of the developed assay, including short analysis time, make it suitable for high throughput targeted UPLC-ESI-MS/MS metabonomic analysis in clinical and epidemiological environments.
Kindinger LM, Bennett PR, Lee YS, et al., 2017, The interaction between vaginal microbiota, cervical length and vaginal progesterone treatment for preterm birth risk, Microbiome, Vol: 5, ISSN: 2049-2618
Background:Preterm birth is the primary cause of infant death worldwide. A short cervix in the second trimester of pregnancy is a risk factor for preterm birth. In specific patient cohorts, vaginal progesterone reduces this risk. Using 16S rRNA gene sequencing we undertook a prospective study in women at risk of preterm birth(n=161) to assess 1) the relationship between vaginal microbiotaand cervical length in the second trimester and preterm birth-risk, and 2) the impact of vaginal progesterone on vaginal bacterial communities in women with a short cervix.Results:Lactobacillus iners dominance at 16 weeks gestation was significantly associated with both a short cervix <25mm (n=15, P<0.05), andpreterm birth <34+0 weeks (n=18, 38P<0.01; 69% PPV).In contrast, L. crispatus dominance was highly predictive of term birth (n=127, 98% PPV). Cervical shortening and preterm birthwere not associated with vaginal dysbiosis. A longitudinal characterization of vaginal microbiota (<18, 22, 28 and 34 weeks)was then undertaken in women receiving vaginal progesterone (400mg/OD, n=25) versus controls (n=42).Progesterone did not alter vaginal bacterial community structurenor reduce L. iners-associated preterm birth (<34 weeks). Conclusions:L. iners dominance of the vaginal microbiota at 16 weeks gestation is a risk factor for preterm birth, whereas L. crispatus dominance is protective against preterm birth. Vaginal progesterone does not appear to impact the pregnancy vaginal microbiota. Patients and clinicians who may be concerned about ‘infection risk’ associated with use of a vaginal pessary during high-risk pregnancy can be reassured.
Chan Q, Loo RL, Ebbels TMD, et al., 2016, Metabolic phenotyping for discovery of urinary biomarkers of diet, xenobiotics and blood pressure in the INTERMAP Study: An overview, Hypertension Research, Vol: 40, Pages: 336-345, ISSN: 1348-4214
The aetiopathogenesis of cardiovascular diseases (CVD) is multifactorial. Adverse bloodpressure (BP) is a major independent risk factor for epidemic CVD affecting about 40% of theadult population worldwide and resulting in significant morbidity and mortality. Metabolicphenotyping of biological fluids has proven its application in characterising low moleculeweight metabolites providing novel insights into gene-environmental-gut microbiomeinteraction in relations to a disease state. In this review, we synthesise key results from theInternational Study of Macro/Micronutrients and Blood Pressure (INTERMAP) Study, a cross-sectional epidemiological study of 4,680 men and women aged 40-59 years from Japan, thePeople’s Republic of China, the United Kingdom, and the United States. We describe theadvancements we have made on: 1) analytical techniques for high throughput metabolicphenotyping; 2) statistical analyses for biomarker identification; 3) discovery of unique food-specific biomarkers; and 4) application of metabolome-wide association (MWA) studies togain a better understanding into the molecular mechanisms of cross cultural and regional BPdifferences.
AHMAD MS, Alsaleh M, Kimhofer T, et al., 2016, The Metabolic Phenotype of Obesity in a Saudi Population, Journal of Proteome Research
Rodriguez Martinez A, Ayala R, Posma JM, et al., 2016, MetaboSignal, a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways, Bioinformatics, Vol: 33, Pages: 773-775, ISSN: 1367-4803
MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes.
Veselkov KA, Inglese, Galea D, et al., 2016, Statistical Tools for Molecular Covariance Spectroscopy, Encyclopedia of Spectroscopy and Spectrometry, Editors: Lindon, Tranter, Koppenaal, Publisher: Elsevier B.V., Pages: 243-249, ISBN: 978-0-12-803224-4
One major application of modern spectroscopic and spectrometric techniques is to measure hundreds to thousands of molecules in biological specimens as part of a process of metabolic phenotyping. Statistical spectroscopy covers a range of techniques used for the recovery of correlated intensity patterns within and between molecules. This plays an essential role in the annotation of molecular features of potential biological or diagnostic significance. The article introduces a variety of univariate and multivariate statistical tools for molecular covariance spectroscopy.
Dumas ME, Domange C, Calderari S, et al., 2016, Topological Analysis of Metabolic Networks Integrating Co-Segregating Transcriptomes and Metabolomes in Type 2 Diabetic Rat Congenic Series, Genome Medicine, Vol: 8, ISSN: 1756-994X
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 f
Hoyles L, Jimenez-Pranteda ML, Chilloux J, et al., 2016, Reduction of trimethylamine N-oxide to trimethylamine by the human gut microbiota: supporting evidence for 'metabolic retroversion', Exploring Human Host-Microbiome Interactions in Health and Disease
Dietary methylamines [choline, trimethylamine N-oxide (TMAO), phosphatidylcholine, carnitine] are present in meat, fish, nuts and eggs. Gut bacteria are able to use choline and carnitine in a fermentation-like process, with trimethylamine (TMA) among the main end-products. TMA is transported from the intestine via the hepatic vein to hepatocytes, then converted to TMAO by hepatic flavin-containing monooxygenases. TMAO present in urine and plasma is currently considered a biomarker for non-alcoholic fatty liver disease, insulin resistance and cardiovascular disease. However, circulating TMAO may play roles in protection from hyperammonemia, and glutamate neurotoxicity. Little is known about the reduction of TMAO (predominantly from fish) to TMA and other compounds by the gut microbiota. We screened 66 strains of human-associated gut bacteria on solid and liquid media for their ability to use TMAO, with metabolites in spent media analysed by 1H-NMR. Enterobacteriaceae produced most TMA from TMAO, with caecal/small intestinal isolates of Escherichia coli producing more TMA than their faecal counterparts. Lactic acid bacteria produced increased amounts of lactate and biomass when grown in the presence of TMAO, but did not appear to use TMAO as an alternative electron acceptor. Stimulation of the growth of gut Enterobacteriaceae in the presence of TMAO was confirmed in faeces-inoculated, anaerobic, stirred, pH-controlled fermentation systems. Feeding deuterated TMAO to C57BL6/J mice demonstrated microbial conversion of TMAO to TMA, with uptake of TMA into the bloodstream and its conversion to TMAO. Antibiotic-treated mice lacked microbial activity necessary to convert TMAO to TMA, instead taking up TMAO into the bloodstream by an unknown mechanism. This study demonstrates microbial reduction of TMAO to TMA followed by host-mediated oxidation of TMA to regenerate TMAO, i.e. metabolic retroversion.
Korecka A, Dona A, Lahiri S, et al., 2016, Bidirectional communication between the Aryl hydrocarbon Receptor (AhR) and the microbiome tunes host metabolism., npj Biofilms and Microbiomes, Vol: 2, Pages: 16014-16014, ISSN: 2055-5008
The ligand-induced transcription factor, aryl hydrocarbon receptor (AhR) is known for its capacity to tune adaptive immunity and xenobiotic metabolism-biological properties subject to regulation by the indigenous microbiome. The objective of this study was to probe the postulated microbiome-AhR crosstalk and whether such an axis could influence metabolic homeostasis of the host. Utilising a systems-biology approach combining in-depth 1H-NMR-based metabonomics (plasma, liver and skeletal muscle) with microbiome profiling (small intestine, colon and faeces) of AhR knockout (AhR-/-) and wild-type (AhR+/+) mice, we assessed AhR function in host metabolism. Microbiome metabolites such as short-chain fatty acids were found to regulate AhR and its target genes in liver and intestine. The AhR signalling pathway, in turn, was able to influence microbiome composition in the small intestine as evident from microbiota profiling of the AhR+/+ and AhR-/- mice fed with diet enriched with a specific AhR ligand or diet depleted of any known AhR ligands. The AhR-/- mice also displayed increased levels of corticosterol and alanine in serum. In addition, activation of gluconeogenic genes in the AhR-/- mice was indicative of on-going metabolic stress. Reduced levels of ketone bodies and reduced expression of genes involved in fatty acid metabolism in the liver further underscored this observation. Interestingly, exposing AhR-/- mice to a high-fat diet showed resilience to glucose intolerance. Our data suggest the existence of a bidirectional AhR-microbiome axis, which influences host metabolic pathways.
Wilson ID, Nicholson JK, 2016, Gut microbiome interactions with drug metabolism, efficacy, and toxicity, Translational Research, Vol: 179, Pages: 204-222, ISSN: 1931-5244
The gut microbiota has both direct and indirect effects on drug and xenobiotic metabolisms, and this can have consequences for both efficacy and toxicity. Indeed, microbiome-driven drug metabolism is essential for the activation of certain prodrugs, for example, azo drugs such as prontosil and neoprontosil resulting in the release of sulfanilamide. In addition to providing a major source of reductive metabolizing capability, the gut microbiota provides a suite of additional reactions including acetylation, deacylation, decarboxylation, dehydroxylation, demethylation, dehalogenation, and importantly, in the context of certain types of drug-related toxicity, conjugates hydrolysis reactions. In addition to direct effects, the gut microbiota can affect drug metabolism and toxicity indirectly via, for example, the modulation of host drug metabolism and disposition and competition of bacterial-derived metabolites for xenobiotic metabolism pathways. Also, of course, the therapeutic drugs themselves can have effects, both intended and unwanted, which can impact the health and composition of the gut microbiota with unforeseen consequences.
Alexander J, Gildea L, Balog J, et al., 2016, A novel methodology for in vivo endoscopic phenotyping of colorectal cancer based on real-time analysis of the mucosal lipidome: a prospective observational study of the iKnife, Surgical Endoscopy and Other Interventional Techniques, Vol: 31, Pages: 1361-1370, ISSN: 1432-2218
Background:This pilot study assessed the diagnostic accuracy of rapid evaporative ionization mass spectrometry (REIMS) in colorectal cancer (CRC) and colonic adenomas.Methods:Patients undergoing elective surgical resection for CRC were recruited at St. Mary’s Hospital London and The Royal Marsden Hospital, UK. Ex vivo analysis was performed using a standard electrosurgery handpiece with aspiration of the electrosurgical aerosol to a Xevo G2-S iKnife QTof mass spectrometer (Waters Corporation). Histological examination was performed for validation purposes. Multivariate analysis was performed using principal component analysis and linear discriminant analysis in Matlab 2015a (Mathworks, Natick, MA). A modified REIMS endoscopic snare was developed (Medwork) and used prospectively in five patients to assess its feasibility during hot snare polypectomy.Results:Twenty-eight patients were recruited (12 males, median age 71, range 35–89). REIMS was able to reliably distinguish between cancer and normal adjacent mucosa (NAM) (AUC 0.96) and between NAM and adenoma (AUC 0.99). It had an overall accuracy of 94.4 % for the detection of cancer versus adenoma and an adenoma sensitivity of 78.6 % and specificity of 97.3 % (AUC 0.99) versus cancer. Long-chain phosphatidylserines (e.g., PS 22:0) and bacterial phosphatidylglycerols were over-expressed on cancer samples, while NAM was defined by raised plasmalogens and triacylglycerols expression and adenomas demonstrated an over-expression of ceramides. REIMS was able to classify samples according to tumor differentiation, tumor budding, lymphovascular invasion, extramural vascular invasion and lymph node micrometastases (AUC’s 0.88, 0.87, 0.83, 0.81 and 0.81, respectively). During endoscopic deployment, colonoscopic REIMS was able to detect target lipid species such as ceramides during hot snare polypectomy.Conclusion:REIMS demonstrates high diagnostic accuracy for tumor type and for established histological featur
Kindinger LM, MacIntyre DA, Lee YS, et al., 2016, Relationship between vaginal microbial dysbiosis, inflammation and pregnancy outcomes in cervical cerclage, Science Translational Medicine, Vol: 8, ISSN: 1946-6242
Preterm birth, the leading cause of death in children under five, may be caused by inflammation triggered by ascending vaginal infection. About two million cervical cerclages are performed annually to prevent preterm birth. The procedure is thought to provide structural support and maintain the endocervical mucus plug as a barrier to ascending infection. Two types of suture material are used for cerclage: monofilament or multifilament braided. Braided sutures are most frequently used, though no evidence exists to favor them over monofilament sutures. In this study we assessed birth outcomes in a retrospective cohort of 678 women receiving cervical cerclage in 5 UK university hospitals and showed that braided cerclage was associated with increased intrauterine death (15% v 5%, P = 0.0001) and preterm birth (28% v 17%, P = 0.0006) compared to monofilament suture. To understand the potential underlying mechanism, we performed a prospective, longitudinal study of the vaginal microbiome in women at risk of preterm birth because of short cervical length (≤25 mm) who received braided (n=25) or monofilament (n=24) cerclage under otherwise comparable circumstances. Braided suture induced a persistent shift towards vaginal microbiome dysbiosis characterized by reduced Lactobacillus spp. and enrichment of pathobionts. Vaginal dysbiosis was associated with inflammatory cytokine and interstitial collagenase excretion into cervicovaginal fluid and premature cervical remodeling. Monofilament suture had comparatively minimal impact upon the vaginal microbiome and its interactions with the host. These data provide in vivo evidence that a dynamic shift of the human vaginal microbiome toward dysbiosis correlates with preterm birth.
Montoliu I, Cominetti O, Boulangé CL, et al., 2016, Modeling Longitudinal Metabonomics and Microbiota Interactions in C57BL/6 Mice Fed a High Fat Diet, Analytical Chemistry, Vol: 88, Pages: 7617-7626, ISSN: 0003-2700
Longitudinal studies aim typically at following populations of subjects over time and are important to understand the global evolution of biological processes. When it comes to longitudinal omics data, it will often depend on the overall objective of the study, and constraints imposed by the data, to define the appropriate modeling tools. Here, we report the use of multilevel simultaneous component analysis (MSCA), orthogonal projection on latent structures (OPLS), and regularized canonical correlation analysis (rCCA) to study associations between specific longitudinal urine metabonomics data and microbiome data in a diet-induced obesity model using C57BL/6 mice. 1H NMR urine metabolic profiling was performed on samples collected weekly over a period of 13 weeks, and stool microbial composition was assessed using 16S rRNA gene sequencing at three specific time periods (baseline, first week response, end of study). MSCA and OPLS allowed us to explore longitudinal urine metabonomics data in relation to the dietary groups, as well as dietary effects on body weight. In addition, we report a data integration strategy based on regularized CCA and correlation analyses of urine metabonomics data and 16S rRNA gene sequencing data to investigate the functional relationships between metabolites and gut microbial composition. Thanks to this workflow enabling the breakdown of this data set complexity, the most relevant patterns could be extracted to further explore physiological processes at an anthropometric, cellular, and molecular level.
Jackson FL, Georgakopoulou N, Kaluarachchi M, et al., 2016, Development of a pipeline for exploratory metabolic profiling of infant urine, Journal of Proteome Research, Vol: 15, Pages: 3432-3440, ISSN: 1535-3907
Numerous metabolic profiling pipelines have been developed to characterize the composition ofhuman biofluids and tissues, the vast majority of these being for studies in adults. To accommodatelimited sample volume and to take into account the compositional differences between adult andinfant biofluids, we developed and optimized sample handling and analytical procedures for studyingurine from newborns. A robust pipeline for metabolic profiling using NMR spectroscopy wasestablished, encompassing sample collection, preparation, spectroscopic measurement andcomputational analysis. Longitudinal samples were collected from five infants from birth until 14months of age. Methods of extraction, effects of freezing and sample dilution were assessed andurinary contaminants from breakdown of polymers in a range of diapers and cotton wool balls wereidentified and compared, including propylene glycol, acrylic acid and tert-butanol. Finally,assessment of urinary profiles obtained over the first few weeks of life revealed a dramatic change in composition, with concentrations of phenols, amino acids and betaine altering systematically overthe first few months of life. Therefore, neonatal samples require more stringent standardization ofexperimental design, sample handling and analysis compared to adult samples in order toaccommodate the variability and limited sample volume.
Lewis MR, Pearce JTM, Spagou K, et al., 2016, Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping, Analytical Chemistry, Vol: 88, Pages: 9004-9013, ISSN: 1520-6882
To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography and mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. Yet, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry, exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch
Vorkas PA, Shalhoub J, Lewis MR, et al., 2016, Metabolic Phenotypes of Carotid Atherosclerotic Plaques Relate to Stroke Risk – An Exploratory Study, European Journal of Vascular and Endovascular Surgery, Vol: 52, Pages: 5-10, ISSN: 1532-2165
Objectives: Stroke is a major cause of death and disability. The fact that three-quarters of stroke patients will never have previously manifested cerebrovascular symptoms demonstrates the unmet clinical need for new biomarkers able to stratify patient risk and elucidation of the biological dysregulations. In this study, we assess the utility of comprehensive metabolic phenotyping to provide candidate biomarkers that relate to stroke risk in stenosing carotid plaque tissue samples.Design: Carotid plaque tissue samples were obtained from patients with cerebrovascular symptoms of carotid origin (n=5), and asymptomatic patients (n=5). Two adjacent biological replicates were obtained from each tissue.Materials and Methods: Organic and aqueous metabolite extracts were separately obtained and analysed using two ultra performance liquid chromatography coupled to mass spectrometry metabolic profiling methods. Multivariate and univariate tools were utilised for statistical analysis.Results: The two studied groups demonstrated distinct plaque phenotypes using multivariate data analysis. Univariate statistics also revealed metabolites that differentiated the two groups with a strong statistical significance (p=10-4-10-5). Specifically, metabolites related to the eicosanoid pathway (arachidonic acid and arachidonic acid precursors), and three acylcarnitine species (butyrylcarnitine, hexanoylcarnitine and palmitoylcarnitine), intermediates of the β-oxidation, were detected in higher intensities in symptomatic patients. However, metabolites implicated in the process of cell death, a process known to be upregulated in the formation of the vulnerable plaque, were unaffected.Conclusions: Discrimination between symptomatic and asymptomatic carotid plaque tissue is demonstrated for the first time using metabolic profiling technologies. Two biological pathways (eicosanoid and β-oxidation) were implicated and will be further investigated. These results indicate that metabolic
Mirnezami R, Veselkov K, Strittmatter N, et al., 2016, Spatially resolved profiling of colorectal cancer lipid biochemistry via DESI imaging mass spectrometry to reveal morphology-dependent alterations in fatty acid metabolism, Annual Meeting of the American-Society-of-Clinical-Oncology (ASCO), Publisher: American Society of Clinical Oncology, ISSN: 0732-183X
Background: Lipid metabolic alterations are recognised as potential oncogenic triggers that promote malignant transformation. Here we performed spatially-resolved profiling of lipid signatures in colorectal cancer (CRC) tissue and matched healthy mucosa using desorption electrospray ionisation imaging mass spectrometry (DESI-MSI). The objectives of this study were to comprehensively define the CRC ‘lipidome’ and to assess lipid signatures in discrete histological regions-of-interest, specifically morphologically bland peri-tumoural epithelium (PT-e) and tumour stroma (T-s). Methods: Fresh frozen tissue sections from 42 patients with confirmed CRC were subjected to negative-ion mode DESI-MSI analysis. Mass spectra in the 200-1000 m/zrange were collated from CRC epithelium (CRC-e), PT-e, T-s and healthy tumour-remote epithelium (TR-e). Spectral signatures were subjected to multivariate analysis using a recursive maximum margin criterion (RMMC) algorithm operating in MATLAB. Results: Increased levels of long/very-long chain fatty acids (LCFA/VLCFA) were seen in CRC-e compared with TR-e(AUC = 0.99). Correspondingly, increased expression of lipogenic and elongase enzymes was found on IHC. Transmission electron microscopy was performed to evaluate peroxisomal distribution and morphology in CRC-e, as these organelles metabolise LCFA/VLCFA through β-oxidation, to negligibly low levels, in healthy cells. No discernible difference in peroxisomal distribution, abundance or structure was found between CRC-e and TR-e. PT-e demonstrated a lipid expression pattern almost identical to that of CRC-e, and markedly different from TR-e (AUC = 0.89). Conclusions: A shift towards increased LCFA/VLCFA production may be an important metabolic trait in CRC facilitated through upregulation of de novo lipogenesis and fatty acid elongation and concurrent impairment of peroxisomal β-oxidation. This phenotype was also observed in morphologically bland PT-e, suggesting that
Zou X, Holmes E, Nicholson JK, et al., 2016, Automatic Spectroscopic Data Categorization by Clustering Analysis (ASCLAN): A Data-Driven Approach for Distinguishing Discriminatory Metabolites for Phenotypic Subclasses, Analytical Chemistry, Vol: 88, Pages: 5670-5679, ISSN: 1086-4377
We propose a novel data-driven approach aiming to reliably distinguish discriminatory metabolites from nondiscriminatory metabolites for a given spectroscopic data set containing two biological phenotypic subclasses. The automatic spectroscopic data categorization by clustering analysis (ASCLAN) algorithm aims to categorize spectral variables within a data set into three clusters corresponding to noise, nondiscriminatory and discriminatory metabolites regions. This is achieved by clustering each spectral variable based on the r2 value representing the loading weight of each spectral variable as extracted from a orthogonal partial least-squares discriminant (OPLS-DA) model of the data set. The variables are ranked according to r2 values and a series of principal component analysis (PCA) models are then built for subsets of these spectral data corresponding to ranges of r2 values. The Q2X value for each PCA model is extracted. K-means clustering is then applied to the Q2X values to generate two clusters based on minimum Euclidean distance criterion. The cluster consisting of lower Q2X values is deemed devoid of metabolic information (noise), while the cluster consists of higher Q2X values is then further subclustered into two groups based on the r2 values. We considered the cluster with high Q2X but low r2 values as nondiscriminatory, while the cluster with high Q2X and r2 values as discriminatory variables. The boundaries between these three clusters of spectral variables, on the basis of the r2 values were considered as the cut off values for defining the noise, nondiscriminatory and discriminatory variables. We evaluated the ASCLAN algorithm using six simulated 1H NMR spectroscopic data sets representing small, medium and large data sets (N = 50, 500, and 1000 samples per group, respectively), each with a reduced and full resolution set of variables (0.005 and 0.0005 ppm, respectively). ASCLAN correctly identified all discriminatory metabolites and showed zero fals
Blaise B, Correia G, Tin A, et al., 2016, A novel method for power analysis and sample size determination in metabolic phenotyping, Analytical Chemistry, Vol: 88, Pages: 5179-5188, ISSN: 1520-6882
Estimation of statistical power and sample size is a key aspect of experimental design. However, in metabolic phenotyping, there is currently no accepted approach for these tasks, in large part due to the unknown nature of the expected effect. In such hypothesis free science, neither the number or class of important analytes nor the effect size are known a priori. We introduce a new approach, based on multivariate simulation, which deals effectively with the highly correlated structure and high-dimensionality of metabolic phenotyping data. First, a large data set is simulated based on the characteristics of a pilot study investigating a given biomedical issue. An effect of a given size, corresponding either to a discrete (classification) or continuous (regression) outcome is then added. Different sample sizes are modeled by randomly selecting data sets of various sizes from the simulated data. We investigate different methods for effect detection, including univariate and multivariate techniques. Our framework allows us to investigate the complex relationship between sample size, power, and effect size for real multivariate data sets. For instance, we demonstrate for an example pilot data set that certain features achieve a power of 0.8 for a sample size of 20 samples or that a cross-validated predictivity QY2 of 0.8 is reached with an effect size of 0.2 and 200 samples. We exemplify the approach for both nuclear magnetic resonance and liquid chromatography–mass spectrometry data from humans and the model organism C. elegans.
Gray N, Adesina-Georgiadis K, Chekmeneva E, et al., 2016, Development of a Rapid Microbore Metabolic Profiling (RAMMP) UPLC-MS Approach for High-Throughput Phenotyping Studies., Analytical Chemistry, Vol: 88, Pages: 5742-5751, ISSN: 0003-2700
A rapid gradient microbore UPLC-MS method has been developed to provide a high-throughput analytical platform for the metabolic phenotyping of urine from large sample cohorts. The rapid microbore metabolic profiling (RAMMP) approach was based on scaling a conventional reversed-phase UPLC-MS method for urinary profiling from 2.1 x 100 mm columns to 1 x 50 mm columns, increasing the linear velocity of the solvent, and decreasing the gradient time to provide an analysis time of 2.5 min/sample. Comparison showed that conventional UPLC-MS and rapid gradient approaches provided peak capacities of 150 and 50 respectively, with the conventional method detecting approximately 19,000 features compared to the ca. 6000 found using the rapid gradient method. Similar levels of repeatability were seen for both methods. Despite the reduced peak capacity and the reduction in ions detected, the RAMMP method was able to achieve similar levels of group discrimination as conventional UPLC-MS when applied to rat urine samples obtained from investigative studies on the effects of acute 2-bromophenol and chronic acetaminophen administration. When compared to a direct infusion MS method of similar analysis time the RAMMP method provided superior selectivity. The RAMMP approach provides a robust and sensitive method that is well suited to high-throughput metabonomic analysis of complex mixtures such as urine combined with a five fold reduction in analysis time compared with the conventional UPLC-MS method.
Boulangé CL, Neves AL, Chilloux J, et al., 2016, Impact of the gut microbiota on inflammation, obesity, and metabolic disease, Genome Medicine, Vol: 8, ISSN: 1756-994X
The human gut harbors more than 100 trillion microbial cells, which have an essential role in human metabolic regulation via their symbiotic interactions with the host. Altered gut microbial ecosystems have been associated with increased metabolic and immune disorders in animals and humans. Molecular interactions linking the gut microbiota with host energy metabolism, lipid accumulation, and immunity have also been identified. However, the exact mechanisms that link specific variations in the composition of the gut microbiota with the development of obesity and metabolic diseases in humans remain obscure owing to the complex etiology of these pathologies. In this review, we discuss current knowledge about the mechanistic interactions between the gut microbiota, host energy metabolism, and the host immune system in the context of obesity and metabolic disease, with a focus on the importance of the axis that links gut microbes and host metabolic inflammation. Finally, we discuss therapeutic approaches aimed at reshaping the gut microbial ecosystem to regulate obesity and related pathologies, as well as the challenges that remain in this area.
Sen A, Knappy C, Lewis MR, et al., 2016, Analysis of polar urinary metabolites for metabolic phenotyping using supercritical fluid chromatography and mass spectrometry, Journal of Chromatography A, Vol: 1449, Pages: 141-155, ISSN: 0412-3425
Supercritical fluid chromatography (SFC) is frequently used for the analysis and separation of non-polar metabolites, but remains relatively underutilised for the study of polar molecules, even those which pose difficulties with established reversed-phase (RP) or hydrophilic interaction liquid chromatographic (HILIC) methodologies. Here, we present a fast SFC-MS method for the analysis of medium and high-polarity (−7 ≤ cLogP ≤ 2) compounds, designed for implementation in a high-throughput metabonomics setting. Sixty polar analytes were first screened to identify those most suitable for inclusion in chromatographic test mixtures; then, a multi-dimensional method development study was conducted to determine the optimal choice of stationary phase, modifier additive and temperature for the separation of such analytes using SFC. The test mixtures were separated on a total of twelve different column chemistries at three different temperatures, using CO2-methanol-based mobile phases containing a variety of polar additives. Chromatographic performance was evaluated with a particular emphasis on peak capacity, overall resolution, peak distribution and repeatability. The results suggest that a new generation of stationary phases, specifically designed for improved robustness in mixed CO2-methanol mobile phases, can improve peak shape, peak capacity and resolution for all classes of polar analytes. A significant enhancement in chromatographic performance was observed for these urinary metabolites on the majority of the stationary phases when polar additives such as ammonium salts (formate, acetate and hydroxide) were included in the organic modifier, and the use of water or alkylamine additives was found to be beneficial for specific subsets of polar analytes. The utility of these findings was confirmed by the separation of a mixture of polar metabolites in human urine using an optimised 7 min gradient SFC method, where the use of the recommended column and co-solv
Phetcharaburanin J, Lees H, Marchesi JR, et al., 2016, Systemic Characterization of an Obese Phenotype in the Zucker Rat Model Defining Metabolic Axes of Energy Metab-olism and Host-Microbial Interactions, Journal of Proteome Research, Vol: 15, Pages: 1897-1906, ISSN: 1535-3907
The Zucker (fa/fa) rat is a valuable and extensively utilized model for obesity research. However, the metabolicnetworks underlying the systemic response in the obese Zucker rats remain to be elucidated. This information is importantto further our understanding of the circulation of the microbial or host-microbial metabolites and their impact on hostmetabolism. 1H Nuclear Magnetic Resonance spectroscopy-based metabolic profiling was used to probe global metabolicdifferences in portal vein and peripheral blood plasma, urine and fecal water between obese (fa/fa, n=12) and lean (fa/+,n=12) Zucker rats. Urinary concentrations of host-microbial co-metabolites were found to be significantly higher in leanZucker rats. Higher concentrations of fecal lactate, short chain fatty acids (SCFAs), 3-hydroxyphenyl propionic acid andglycerol, and lower levels of valine and glycine were observed in obese rats compared with lean animals. Regardless ofphenotype, concentrations of SCFAs, tricarboxylic acid cycle intermediates, and choline metabolites were higher in portalvein blood compared to peripheral blood. However, higher levels of succinate, phenylalanine and tyrosine were observedin portal vein blood compared with peripheral blood from lean rats but not in obese rats. Our findings indicate that theabsorption of propionate and acetate, choline and TMA are independent of the Zucker rat phenotypes. However, urinaryhost-microbial co-metabolites were highly associated with phenotypes, suggesting distinct gut microbial metabolic activitiesin lean and obese Zucker rats. This work advances our understanding of metabolic processes associated with obesity,particularly the metabolic functionality of the gut microbiota in the context of obesity.
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