Publications
1002 results found
Posma JM, Garcia Perez I, Ebbels TMD, et al., 2018, Optimized phenotypic biomarker discovery and confounder elimination via covariate-adjusted projection to latent structures from metabolic spectroscopy data, Journal of Proteome Research, Vol: 17, Pages: 1586-1595, ISSN: 1535-3893
Metabolism is altered by genetics, diet, disease status, environment and many other factors. Modelling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-Adjusted Projections to Latent Structures (CA-PLS) is exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.
Nicholson J, Rampersad N, 2018, The Frobenius problem for the shuffle operation, SEMIGROUP FORUM, Vol: 96, Pages: 160-177, ISSN: 0037-1912
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MacIntyre DA, Brown R, Marchesi J, et al., 2018, Vaginal dysbiosis increases risk of preterm fetal membrane rupture, neonatal sepsis and is exacerbated by erythromycin, BMC Medicine, Vol: 16, ISSN: 1741-7015
Background: Preterm prelabour rupture of the fetal membranes (PPROM) precedes 30% of preterm births and is a risk factor for early onset neonatal sepsis. As PPROM is strongly associated with ascending vaginal infection prophylactic antibiotics are widely used. The evolution of vaginal microbiota composition associated with PPROM and the impact of antibiotics on bacterial composition is unknown. Methods: We prospectively assessed vaginal microbiota prior to and following PPROM using MiSeq-based sequencing of 16S rRNA gene amplicons and examined the impact of erythromycin prophylaxis on bacterial load and community structures.Results: In contrast to pregnancies delivering at term, vaginal dysbiosis characterised by Lactobacillus spp. depletion, was present prior to the rupture of fetal membranes in approximately a third of cases (0% versus 27%, P= 0.026) and persisted following membrane rupture (31%, P= 0.005). Vaginal dysbiosis was exacerbated by erythromycin treatment (47%, P= 0.00009) particularly in women initially colonised by Lactobacillus species. Lactobacillus depletion and increased relative abundance of Sneathia spp. was associated with subsequent funisitis and early onset neonatal sepsis. Conclusions:Our data show that vaginal microbiota composition is a risk-factor for subsequent PPROM and is associated with adverse short-term maternal and neonatal outcomes. This highlights vaginal microbiota as a potentially modifiable antenatal risk factor for PPROM and suggests that routine use of erythromycin for PPROM be re-examined.
Enzi MM, Nicholson J, Shukurova M, 2018, Acetosyringone's Effect on Agrobacterium-mediated Transformation of Eustoma grandiflorum Leaf Disks, CCAMLR SCIENCE, Vol: 25, Pages: 151-156, ISSN: 1023-4063
Nicholson J, Rampersad N, 2018, Improved Estimates for the Number of Privileged Words, JOURNAL OF INTEGER SEQUENCES, Vol: 21, ISSN: 1530-7638
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Hoyles L, Jiménez-Pranteda ML, Chilloux J, et al., 2017, Metabolic retroconversion of trimethylamine <i>N</i>-oxide and the gut microbiota
<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>BACKGROUND</jats:title><jats:p>The dietary methylamines choline, carnitine and phosphatidylcholine are used by the gut microbiota to produce a range of metabolites, including trimethylamine (TMA). However, little is known about the use of trimethylamine <jats:italic>N</jats:italic>-oxide (TMAO) by this consortium of microbes.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>A feeding study using deuterated TMAO in C57BL6/J mice demonstrated microbial conversion of TMAO to TMA, with uptake of TMA into the bloodstream and its conversion to TMAO. Microbial activity necessary to convert TMAO to TMA was suppressed in antibiotic-treated mice, with deuterated TMAO being taken up directly into the bloodstream. In batch-culture fermentation systems inoculated with human faeces, growth of <jats:italic>Enterobacteriaceae</jats:italic> was stimulated in the presence of TMAO. Human-derived faecal and caecal bacteria (<jats:italic>n</jats:italic> = 66 isolates) were screened on solid and liquid media for their ability to use TMAO, with metabolites in spent media analysed by <jats:sup>1</jats:sup>H-NMR. As with the <jats:italic>in vitro</jats:italic> fermentation experiments, TMAO stimulated the growth of <jats:italic>Enterobacteriaceae</jats:italic>; these bacteria produced most TMA from TMAO. Caecal/small intestinal isolates of <jats:italic>Escherichia coli</jats:italic> produced more TMA from TMAO than their faecal counterparts. Lactic acid bacteria produced increased amounts of lactate when grown in the presence of TMAO, but did not produce large amounts of TMA. Clostridia (<jats:italic>sensu stricto</jats:italic>), bifidobacteria and coriobacteria were significantly correlated with TMA production in the mixed fermentation system but did
Galea D, Inglese P, Cammack L, et al., 2017, Translational utility of a hierarchical classification strategy in biomolecular data analytics., Scientific Reports, Vol: 7, ISSN: 2045-2322
Hierarchical classification (HC) stratifies and classifies data from broad classes into more specific classes. Unlike commonly used data classification strategies, this enables the probabilistic prediction of unknown classes at different levels, minimizing the burden of incomplete databases. Despite these advantages, its translational application in biomedical sciences has been limited. We describe and demonstrate the implementation of a HC approach for "omics-driven" classification of 15 bacterial species at various taxonomic levels achieving 90-100% accuracy, and 9 cancer types into morphological types and 35 subtypes with 99% and 76% accuracy, respectively. Unknown bacterial species were probabilistically assigned with 100% accuracy to their respective genus or family using mass spectra (n = 284). Cancer types were predicted by mRNA data (n = 1960) for most subtypes with 95-100% accuracy. This has high relevance in clinical practice where complete datasets are difficult to compile with the continuous evolution of diseases and emergence of new strains, yet prediction of unknown classes, such as bacterial species, at upper hierarchy levels may be sufficient to initiate antimicrobial therapy. The algorithms presented here can be directly translated into clinical-use with any quantitative data, and have broad application potential, from unlabeled sample identification, to hierarchical feature selection, and discovery of new taxonomic variants.
Garcia Perez I, Posma JM, Gibson R, et al., 2017, Modernizing dietary assessment, IUNS 21st ICN International Congress of Nutrition, Publisher: Karger Publishers, Pages: 286-287, ISSN: 1421-9697
Rodriguez-Martinez A, Posma JM, Ayala R, et al., 2017, J-Resolved (1)H NMR 1D-Projections for Large-Scale Metabolic Phenotyping Studies: Application to Blood Plasma Analysis., Analytical Chemistry, Vol: 89, Pages: 11405-11412, ISSN: 0003-2700
(1)H nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping is now widely used for large-scale epidemiological applications. To minimize signal overlap present in 1D (1)H NMR spectra, we have investigated the use of 2D J-resolved (JRES) (1)H NMR spectroscopy for large-scale phenotyping studies. In particular, we have evaluated the use of the 1D projections of the 2D JRES spectra (pJRES), which provide single peaks for each of the J-coupled multiplets, using 705 human plasma samples from the FGENTCARD cohort. On the basis of the assessment of several objective analytical criteria (spectral dispersion, attenuation of macromolecular signals, cross-spectral correlation with GC-MS metabolites, analytical reproducibility and biomarker discovery potential), we concluded that the pJRES approach exhibits suitable properties for implementation in large-scale molecular epidemiology workflows.
Bray R, Cacciatore S, Jimenez B, et al., 2017, Urinary metabolic phenotyping of women with lower urinary tract symptoms, Journal of Proteome Research, Vol: 16, Pages: 4208-4216, ISSN: 1535-3893
Lower urinary tract symptoms (LUTS), including urinary incontinence, urgency and nocturia, affect approximately half of women worldwide. Current diagnostic methods for LUTS are invasive and costly, while available treatments are limited by side effects leading to poor patient compliance. In this study, we aimed to identify urine metabolic signatures associated with LUTS using proton nuclear magnetic resonance (1H NMR) spectroscopy. A total of 214 urine samples were collected from women attending tertiary urogynecology clinics (cases; n = 176) and healthy control women attending general gynecology clinics (n = 36). Despite high variation in the urine metabolome across the cohort, associations between urine metabolic profiles and BMI, parity, overactive bladder syndrome, frequency, straining, and bladder storage were identified using KODAMA (knowledge discovery by accuracy maximization). Four distinct urinary metabotypes were identified, one of which was associated with increased urinary frequency and low BMI. Urine from these patients was characterized by increased levels of isoleucine and decreased levels of hippurate. Our study suggests that metabolic profiling of urine samples from LUTS patients offers the potential to identify differences in underlying etiology, which may permit stratification of patient populations and the design of more personalized treatment strategies.
Hoyles L, Fernández-Real JM, Federici M, et al., 2017, Integrated systems biology to study the contribution of the gut microbiome to steatosis in obese women, Exploring Human Host-Microbiome Interactions in Health and Disease
Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease, increasing in worldwide prevalence as a result of the obesity epidemic. It manifests in hepatic cells as steatosis with or without lobular inflammation and/or ballooning. Animal and human studies have suggested the gut microbiome contributes to steatosis/NAFLD. The aim of this study was to use an integrated approach with various -omics and clinical data to evaluate the contribution of the gut microbiome to the molecular phenome (hepatic transcriptome, metabonome) of steatosis. Metagenomic (faecal microbiome), transcriptomic (liver biopsy), metabonomic (plasma and urine, 1H-NMR) and clinical data were collected for 56 morbidly obese (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 modelling. Histological examination of liver biopsies was used to grade steatosis. Faecal metagenomes were generated and analysed using the SCalable Automated Metagenomics Pipeline (SCAMP). Differentially expressed genes were identified in hepatic transcriptomes, and analysed using a range of different bioinformatics tools. 1H-NMR data were generated for plasma and urinary metabonomes. Clinical, metagenomic, transcriptomic and metabonomic data were integrated in the context of steatosis using partial Spearman's correlation, taking confounders (age, body mass index and cohort) into account. Steatosis 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. Steatosis-associated hepatic transcriptomes were associated with branched-chain amino acid (BCAA) metabolism, endoplasmic reticulum/phagosome, and immune responses associated with non-specific microbial infections. Metabonom
Hoyles L, Snelling T, Umlai UK, et al., 2017, Propionate has protective and anti-inflammatory effects on the blood–brain barrier, Exploring Human Host-Microbiome Interactions in Health and Disease
Production of short-chain fatty acids (SCFAs) from dietary substrates by the gut microbiota is associated with health, with these metabolites influencing the host via the ‘gut–brain axis’. Micromolar quantities of microbially derived SCFAs are taken up from the gut and reach systemic circulation, where they can influence host gene expression through a variety of largely unknown mechanisms. The blood–brain barrier (BBB) is the major interface between the circulation and central nervous system, and is critically involved in the pathogenesis of neuroinflammatory disorders such as stroke and vascular dementia. We hypothesized exposure of the BBB to SCFAs influences barrier integrity and function.To test our hypothesis, we investigated the in vitro effects of a physiologically relevant concentration (1 μM) of propionate upon the human immortalised cerebromicrovascular endothelial cell line hCMEC/D3. Propionate is produced by the microbiota from dietary glucans, and is biologically active via the G protein coupled receptors FFAR2 and FFAR3. It is a highly potent FFAR2 agonist (agonist activity 3.99) and has close to optimal ligand efficiency (-ΔG=1.19 kcal mol-1 atom-1) for this receptor. Notably, FFAR3 is expressed on the vascular endothelium and a likely target for propionate in the BBB.After confirming the presence of FFAR3 on hCMEC/D3 cells, we undertook an unbiased transcriptomic analysis of confluent hCMEC/D3 monolayers treated or not for 24 h with 1 μM propionate, supported by in vitro validation of key findings and assessment of functional endothelial permeability barrier properties.Propionate treatment had a significant (PFDR < 0.1) effect on the expression of 1136 genes: 553 upregulated, 583 downregulated. Propionate inhibited several inflammation-associated pathways: namely, TLR-specific signalling, NFkappaB signalling, and cytosolic DNA-sensing. Functional validation of these findings confirmed the down-regulation of TLR
Kinross J, Mirnezami R, Alexander J, et al., 2017, A prospective analysis of mucosal microbiome-metabonome interactions in colorectal cancer using a combined MAS 1HNMR and metataxonomic strategy, Scientific Reports, Vol: 7, ISSN: 2045-2322
Colon cancer induces a state of mucosal dysbiosis with associated niche specific changes in the gut microbiota. However, the key metabolic functions of these bacteria remain unclear. We performed a prospective observational study in patients undergoing elective surgery for colon cancer without mechanical bowel preparation (n = 18). Using 16 S rRNA gene sequencing we demonstrated that microbiota ecology appears to be cancer stage-specific and strongly associated with histological features of poor prognosis. Fusobacteria (p < 0.007) and ε- Proteobacteria (p < 0.01) were enriched on tumour when compared to adjacent normal mucosal tissue, and fusobacteria and β-Proteobacteria levels increased with advancing cancer stage (p = 0.014 and 0.002 respecitvely). Metabonomic analysis using 1H Magic Angle Spinning Nuclear Magnetic Resonsance (MAS-NMR) spectroscopy, demonstrated increased abundance of taurine, isoglutamine, choline, lactate, phenylalanine and tyrosine and decreased levels of lipids and triglycerides in tumour relative to adjacent healthy tissue. Network analysis revealed that bacteria associated with poor prognostic features were not responsible for the modification of the cancer mucosal metabonome. Thus the colon cancer mucosal microbiome evolves with cancer stage to meet the demands of cancer metabolism. Passenger microbiota may play a role in the maintenance of cancer mucosal metabolic homeostasis but these metabolic functions may not be stage specific.
castagne R, Boulange CL, Karaman I, et al., 2017, Improving visualisation and interpretation of metabolome-wide association studies (MWAS): an application in a population-based cohort using untargeted 1H NMR metabolic profiling., Journal of Proteome Research, Vol: 16, Pages: 3623-3633, ISSN: 1535-3893
1H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.
Rodriguez Martinez A, Posma JM, Ayala R, et al., 2017, MWASTools: an R/Bioconductor package for metabolome-wide association studies, Bioinformatics, Vol: 34, Pages: 890-892, ISSN: 1367-4803
Summary: MWASTools is an R package designed to provide an integrated pipeline to analyze metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of MWAS results.Availability: The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/
Lewis MC, Merrifield CA, Berger B, et al., 2017, Early intervention with Bifidobacterium lactis NCC2818 modulates the host-microbe interface independent of the sustained changes induced by the neonatal environment., Scientific Reports, Vol: 7, ISSN: 2045-2322
Inflammatory and metabolic diseases can originate during early-life and have been correlated with shifts in intestinal microbial ecology. Here we demonstrate that minor environmental fluctuations during the early neonatal period had sustained effects on the developing porcine microbiota and host-microbe interface. These inter-replicate effects appear to originate during the first day of life, and are likely to reflect very early microbiota acquisition from the environment. We statistically link early systemic inflammation with later local increases in inflammatory cytokine (IL-17) production, which could have important enteric health implications. Immunity, intestinal barrier function, host metabolism and host-microbiota co-metabolism were further modified by Bifidobacterium lactis NCC2818 supplementation, although composition of the in situ microbiota remained unchanged. Finally, our robust model identified novel, strong correlations between urinary metabolites (eg malonate, phenylacetylglycine, alanine) and mucosal immunoglobulin (IgM) and cytokine (IL-10, IL-4) production, thus providing the possibility of the development of urinary 'dipstick' tests to assess non-accessible mucosal immune development and identify early precursors (biomarkers) of disease. These results have important implications for infants exposed to neonatal factors including caesarean delivery, antibiotic therapy and delayed discharge from hospital environments, which may predispose to the development of inflammatory and metabolic diseases in later life.
Dumas M, Rothwell AR, Hoyles L, et al., 2017, Microbial-host co-metabolites are prodromal markers predicting phenotypic heterogeneity in behavior, obesity and impaired glucose tolerance, Cell Reports, Vol: 20, Pages: 136-148, ISSN: 2211-1247
The influence of the gut microbiome on metabolic and behavioral traits is now widely accepted, though the microbiome-derived metabolites involved remain unclear. We carried out untargeted urine 1H NMR spectroscopy-based metabolic phenotyping in an isogenic C57BL/6J mouse population (n=50) and show that microbial-host co-metabolites are prodromal (i.e., early) markers predicting future divergence in metabolic (obesity and glucose homeostasis) and behaviorial (anxiety and activity) outcomes with 94-100% accuracy. Some of these metabolites also modulate disease phenotypes, best illustrated by trimethylamine-N-oxide (TMAO), a product of microbial-host co-metabolism predicting future obesity, impaired glucose tolerance (IGT) and behavior, whilst reducing endoplasmic reticulum stress and lipogenesis in 3T3-L1 adipocytes. Chronic in vivo TMAO treatment limits IGT in HFD-fed mice and isolated pancreatic islets by increasing insulin secretion. We highlight the prodromal potential of microbial metabolites to predict disease outcomes and their potential in shaping mammalian phenotypic heterogeneity.
Hoyles L, Fernández-Real JM, Federici M, et al., 2017, Integrated systems biology to study non-alcoholic fatty liver disease in obese women, International Scientific Association for Probiotics and Prebiotics
Metagenomic (faecal microbiome), transcriptomic (liver biopsy), metabonomic (plasma and urine, 1H-NMR) and clinical (28 variables) data were collected for 56 morbidly obese (BMI >35) women from Italy (n = 31) and Spain (n = 25) who elected for bariatric surgery. Data were integrated to evaluate the contribution of the gut microbiome to the molecular phenome (hepatic transcriptome, plasma and urine metabonome) of NAFLD independent of clinical confounders (age, BMI, cohort) using partial Spearman’s correlation. NAFLD activity score (NAS) 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 (BCAA) metabolism and gut-derived microbial metabolites resulting from metabolism of amino acids. NAFLD-associated hepatic transcriptomes were associated with BCAA metabolism, endoplasmic reticulum/phagosome, and immune responses associated with non-specific microbial infections. Molecular phenomic signatures were stable and predictive regardless of sample size, and consistent with the microbiome making a significant contribution to the NAFLD phenome. There is disruption of the gut– liver axis in NAFLD, which can be seen in the gut microbiome, hepatic transcriptome and urinary and plasma metabonomes. Consistency of phenome signatures strongly supports a relationship between microbial amino acid metabolism and microbial gene richness, hepatic gene expression and biofluid metabonomes, and ultimately NAS.
Rainville PD, Wilson ID, Nicholson JK, et al., 2017, Ion Mobility Spectrometry Combined With Ultra Performance Liquid Chromatography/Mass Spectrometry For Metabolic Phenotyping of Urine: Effects of Column Length, Gradient Duration and Ion Mobility Spectrometry on Metabolite Detection., Analytica Chimica Acta, Vol: 982, Pages: 1-8, ISSN: 1873-4324
The need for rapid and efficient high throughput metabolic phenotyping (metabotyping) in metabolomic/metabonomic studies often requires compromises to be made between analytical speed and metabolome coverage. Here the effect of column length (150, 75 and 30 mm) and gradient duration (15, 7.5 and 3 min respectively) on the number of features detected when untargeted metabolic profiling of human urine using reversed-phase gradient ultra performance chromatography with, and without, ion mobility spectrometry, has been examined. As would be expected, reducing column length from 150 to 30 mm, and gradient duration, from 15 to 3 min, resulted in a reduction in peak capacity from 311 to 63 and a similar reduction in the number of features detected from over ca. 16,000 to ca. 6500. Under the same chromatographic conditions employing UPLC/IMS/MS to provide an additional orthogonal separation resulted in an increase in the number of MS features detected to nearly 20,000 and ca. 7500 for the 150 mm and the 30 mm columns respectively. Based on this limited study the potential of LC/IMS/MS as a tool for improving throughput and increasing metabolome coverage clearly merits further in depth study.
Hoyles L, Fernandez-Real JM, Federici M, et al., 2017, Integrated systems biology to study non-alcoholic fatty liver disease in obese women, Tranlsational Bioinformatics
Non-alcoholic fatty liver disease (NAFLD) is a multifactorial condition and one of the most common causes of chronic liver disease, with increasing worldwide prevalence. Microbiome-associated lipopolysaccharides (LPS) are associated with NAFLD in rodent models, but their relevance in human liver disease is not understood. In addition, microbiome-driven degradation of dietary choline – and its subsequent removal from host-associated metabolic processes – is thought to contribute to development of NAFLD. The FLORINASH study set out to determine the contribution of the gut microbiome to the NAFLD-associated molecular phenome (transcriptome, metabonome) independent of clinical confounders.Morbidly obese women [body mass index (BMI) >35] from Italy (n = 31) and Spain (n = 25) who elected for bariatric surgery were recruited to the study. Clinical data (28 variables) were recorded. Faecal samples, liver biopsies, blood and urine samples were collected. Faecal metagenomes were analysed using an in-house metagenomics pipeline (SCaleble Automated Metagenomics Pipeline). NAFLD activity score (NAS; 0, 1, 2, 3) was determined by histological examination of liver biopsies. Differentially expressed genes in hepatic transcriptomes were identified, and analysed using several complementary tools. 1H-NMR data were generated for plasma and urinary metabonomes. Clinical, metagenomic, transcriptomic and metabonomic data were integrated using partial Spearman’s correlation, taking identified confounders (age, BMI and cohort) into account.NAS was anti-correlated with microbial gene richness, and correlated with abundance of Gram-negative Proteobacteria. KEGG analyses of metagenomic data suggested increased microbial processing of dietary lipids and amino acids, as well as LPS-related processes associated with Proteobacteria in NAFLD. Activation of immune responses associated with Gram-negative (LPS-associated) microbial infections was correlated with NAS in hepatic tr
Swann JR, Garcia-Perez I, Braniste V, et al., 2017, Application of 1H NMR spectroscopy to the metabolic phenotyping of rodent brain extracts: a metabonomic study of gut microbial influence on host brain metabolism, Journal of Pharmaceutical and Biomedical Analysis, Vol: 143, Pages: 141-146, ISSN: 1873-264X
H NMR Spectroscopy has been applied to determine the neurochemical profiles of brain extracts from the frontal cortex and hippocampal regions of germ free and normal mice and rats. The results revealed a number of differences between germ free (GF) and conventional (CV) rats or specific pathogen-free (SPF) mice with microbiome-associated metabolic variation found to be both species- and region-dependent. In the mouse, the GF frontal cortex contained lower amounts of creatine, N-acetyl-aspartate (NAA), glycerophosphocholine and lactate, but greater amounts of choline compared to that of specific pathogen free (SPF) mice. In the hippocampus, the GF mice had greater creatine, NAA, lactate and taurine content compared to those of the SPF animals, but lower relative quantities of succinate and an unidentified lipid-related component. The GF rat frontal cortex contained higher relative quantities of lactate, creatine and NAA compared to the CV animals whilst the GF hippocampus was characterized by higher taurine and phosphocholine concentrations and lower quantities of NAA, N-acetylaspartylglutamate and choline compared to the CV animals. Of note is that, in both rat and mouse brain extracts, concentrations of hippocampal taurine were found to be greater in the absence of an established microbiome. The results provide further evidence that brain biochemistry can be influenced by gut microbial status, specifically metabolites involved in energy metabolism demonstrating biochemical dialogue between the microbiome and brain.
Liu J, Suel G, Stevens TJ, et al., 2017, Principles of Systems Biology, No. 17, CELL SYSTEMS, Vol: 4, Pages: 472-475, ISSN: 2405-4712
Wolfer AM, Scott AJ, Rueb C, et al., 2017, Longitudinal analysis of serum oxylipin profile as a novel descriptor of the inflammatory response to surgery, JOURNAL OF TRANSLATIONAL MEDICINE, Vol: 15, ISSN: 1479-5876
Background:Oxylipins are potent lipid mediators demonstrated to initiate and regulate inflammation yet little is known regarding their involvement in the response to surgical trauma. As key modulators of the inflammatory response, oxylipins have the potential to provide novel insights into the physiological response to surgery and the pathophysiology of post-operative complications. We aimed to investigate the effects of major surgery on longitudinal oxylipin profile.Methods:Adults patients undergoing elective laparoscopic or open colorectal resections were included. Primary outcomes were serum oxylipin profile quantified by ultra high-performance liquid chromatography-mass spectrometry, serum white cell count and C-reactive protein concentration. Serum samples were taken at three time-points: pre-operative (day zero), early post-operative (day one) and late post-operative (day four/five).Results:Some 55 patients were included, of which 33 (60%) underwent surgery that was completed laparoscopically. Pre-operative oxylipin profiles were characterised by marked heterogeneity but surgery induced a common shift resulting in more homogeneity at the early post-operative time-point. By the late post-operative phase, oxylipin profiles were again highly variable. This evolution was driven by time-dependent changes in specific oxylipins. Notably, the levels of several oxylipins with anti-inflammatory properties (15-HETE and four regioisomers of DHET) were reduced at the early post-operative point before returning to baseline by the late post-operative period. In addition, levels of the pro-inflammatory 11-HETE rose in the early post-operative phase while levels of anti-thrombotic mediators (9-HODE and 13-HODE) fell; concentrations of all three oxylipins then remained fairly static from early to late post-operative phases. Compared to those undergoing laparoscopic surgery, patients undergoing open surgery had lower levels of some anti-inflammatory oxylipins (8,9-DHET and 17-HD
Poynter LR, Veselkov K, Galea D, et al., 2017, Network-driven analytics of published tissue-based biomarkers to predict response to neoadjuvant therapy in rectal cancer, Annual Meeting of the American-Association-for-Cancer-Research (AACR), Publisher: AMER ASSOC CANCER RESEARCH, ISSN: 0008-5472
Xie G, Wang X, Zhao A, et al., 2017, Sex-dependent effects on gut microbiota regulate hepatic carcinogenic outcomes, SCIENTIFIC REPORTS, Vol: 7, ISSN: 2045-2322
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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., Nature Reviews Gastroenterology and Hepatology, Vol: 14, Pages: 356-365, ISSN: 1759-5045
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.
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