37 results found
Garcia Perez I, Posma JM, Serrano Contreras JI, et al., 2020, Identifying unknown metabolites using NMR-based metabolic profiling techniques, Nature Protocols, Vol: 15, Pages: 2538-2567, ISSN: 1750-2799
Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes, but is hindered by a lack of automated annotation and standardised methods for structure elucidation of candidate disease biomarkers. Here, we describe a system for identifying molecular species derived from NMR spectroscopy based metabolic phenotyping studies, with detailed info on sample preparation, data acquisition, and data modelling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as STOCSY, STORM and RED-STORM to identify other signals in the NMR spectra relating to the same molecule. It also utilizes 2D-NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multidimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take two or three days. This approach to biomarker discovery is efficient, cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. Finally, it requires basic understanding of Matlab in order to perform statistical spectroscopic tools and analytical skills to perform Solid Phase Extraction, LC-fraction collection, LC-NMR-MS and 1D and 2D NMR experiments.
Eriksen R, Garcia Perez I, Posma JM, et al., 2020, Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study, EBioMedicine, Vol: 58, Pages: 1-9, ISSN: 2352-3964
BackgroundDietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D.MethodsWe analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models.FindingsA higher Tpred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=–0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glu
Posma JM, Garcia Perez I, Frost G, et al., 2020, Nutriome-metabolome relationships provide insights into dietary intake and metabolism, Nature Food, Vol: 1, Pages: 426-436, ISSN: 2662-1355
Dietary assessment traditionally relies on self-reported data which are often inaccurate and may result in erroneous diet-disease risk associations. We illustrate how urinary metabolic phenotyping can be used as alternative approach for obtaining information on dietary patterns. We used two multi-pass 24-hr dietary recalls, obtained on two occasions on average three weeks apart, paired with two 24-hr urine collections from 1,848 U.S. individuals; 67 nutrients influenced the urinary metabotype measured with ¹H-NMR spectroscopy characterized by 46 structurally identified metabolites. We investigated the stability of each metabolite over time and showed that the urinary metabolic profile is more stable within individuals than reported dietary patterns. The 46 metabolites accurately predicted healthy and unhealthy dietary patterns in a free-living U.S. cohort and replicated in an independent U.K. cohort. We mapped these metabolites into a host-microbial metabolic network to identify key pathways and functions. These data can be used in future studies to evaluate how this set of diet-derived, stable, measurable bioanalytical markers are associated with disease risk. This knowledge may give new insights into biological pathways that characterize the shift from a healthy to unhealthy metabolic phenotype and hence give entry points for prevention and intervention strategies.
Garcia Perez I, Posma JM, Chambers E, et al., 2020, Dietary metabotype modelling predicts individual responses to dietary interventions, Nature Food, Vol: 1, Pages: 355-364, ISSN: 2662-1355
Habitual consumption of poor quality diets is linked directly to risk factors for many non-communicable disease. This has resulted in the vast majority of countries globally and the World Health Organisation developing policies for healthy eating to reduce the prevalence of non communicable disease in the population. However, there is mounting evidence of variability in individual metabolic responses to any dietary intervention. We have developed a method for applying a pipeline for understanding inter-individual differences in response to diet, based on coupling data from highly-controlled dietary studies with deep metabolic phenotyping. In this feasibility study, we create an individual Dietary Metabotype Score (DMS) that embodies inter-individual variability in dietary response and captures consequent dynamic changes in concentrations of urinary metabolites. We find an inverse relationship between the DMS and blood glucose concentration. There is also a relationship between the DMS and urinary metabolic energy loss. Furthermore we employ a metabolic entropy approach to visualize individual and collective responses to dietary. Potentially, the DMS offers a method to target and to enhance dietary response at an individual level therefore reducing burden of non communicable diseases at a population level.
Andreas NJ, Roy RB, Gomez-Romero M, et al., 2020, Performance of metabonomic serum analysis for diagnostics in paediatric tuberculosis, SCIENTIFIC REPORTS, Vol: 10, ISSN: 2045-2322
Ocvirk S, Wilson AS, Posma JM, et al., 2019, A prospective cohort analysis of gut microbial co-metabolism in Alaska Native and rural African people at high and low risk of colorectal cancer, American Journal of Clinical Nutrition, Vol: 111, Pages: 406-419, ISSN: 0002-9165
BACKGROUND: Alaska Native (AN) people have the world's highest recorded incidence of sporadic colorectal cancer (CRC) (∼91:100,000), whereas rural African (RA) people have the lowest risk (<5:100,000). Previous data supported the hypothesis that diet affected CRC risk through its effects on the colonic microbiota that produce tumor-suppressive or -promoting metabolites. OBJECTIVES: We investigated whether differences in these metabolites may contribute to the high risk of CRC in AN people. METHODS: A cross-sectional observational study assessed dietary intake from 32 AN and 21 RA healthy middle-aged volunteers before screening colonoscopy. Analysis of fecal microbiota composition by 16S ribosomal RNA gene sequencing and fecal/urinary metabolites by 1H-NMR spectroscopy was complemented with targeted quantification of fecal SCFAs, bile acids, and functional microbial genes. RESULTS: Adenomatous polyps were detected in 16 of 32 AN participants, but not found in RA participants. The AN diet contained higher proportions of fat and animal protein and less fiber. AN fecal microbiota showed a compositional predominance of Blautia and Lachnoclostridium, higher microbial capacity for bile acid conversion, and low abundance of some species involved in saccharolytic fermentation (e.g., Prevotellaceae, Ruminococcaceae), but no significant lack of butyrogenic bacteria. Significantly lower concentrations of tumor-suppressive butyrate (22.5 ± 3.1 compared with 47.2 ± 7.3 SEM µmol/g) coincided with significantly higher concentrations of tumor-promoting deoxycholic acid (26.7 ± 4.2 compared with 11 ± 1.9 µmol/g) in AN fecal samples. AN participants had lower quantities of fecal/urinary metabolites than RA participants and metabolite profiles correlated with the abundance of distinct microbial genera in feces. The main microbial and metabolic CRC-associated markers were not significantly altered in
Wilson T, Garcia-Perez I, Posma JM, et al., 2019, Spot and cumulative urine samples are suitable replacements for 24-hour urine collections for objective measures of dietary exposure in adults using metabolite biomarkers, Journal of Nutrition, Vol: 149, Pages: 1692-1700, ISSN: 0022-3166
BACKGROUND: Measurement of multiple food intake exposure biomarkers in urine may offer an objective method for monitoring diet. The potential of spot and cumulative urine samples that have reduced burden on participants as replacements for 24-h urine collections has not been evaluated. OBJECTIVE: The aim of this study was to determine the utility of spot and cumulative urine samples for classifying the metabolic profiles of people according to dietary intake when compared with 24-h urine collections in a controlled dietary intervention study. METHODS: Nineteen healthy individuals (10 male, 9 female, aged 21-65 y, BMI 20-35 kg/m2) each consumed 4 distinctly different diets, each for 1 wk. Spot urine samples were collected ∼2 h post meals on 3 intervention days/wk. Cumulative urine samples were collected daily over 3 separate temporal periods. A 24-h urine collection was created by combining the 3 cumulative urine samples. Urine samples were analyzed with metabolite fingerprinting by both high-resolution flow infusion electrospray mass spectrometry (FIE-HRMS) and proton nuclear magnetic resonance spectroscopy (1H-NMR). Concentrations of dietary intake biomarkers were measured with liquid chromatography triple quadrupole mass spectrometry and by integration of 1H-NMR data. RESULTS: Cross-validation modeling with 1H-NMR and FIE-HRMS data demonstrated the power of spot and cumulative urine samples in predicting dietary patterns in 24-h urine collections. Particularly, there was no significant loss of information when post-dinner (PD) spot or overnight cumulative samples were substituted for 24-h urine collections (classification accuracies of 0.891 and 0.938, respectively). Quantitative analysis of urine samples also demonstrated the relation between PD spot samples and 24-h urines for dietary exposure biomarkers. CONCLUSIONS: We conclude that PD spot urine samples are suitable replacements for 24-h urine collections. Alternatively, cumulative samples collected overn
Lahiri S, Kim H, Garcia-Perez I, et al., 2019, The gut microbiota influences skeletal muscle mass and function in mice, Science Translational Medicine, Vol: 11, ISSN: 1946-6234
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 <jats:italic>Rapsyn</jats:italic> and <jats:italic>Lrp4</jats:italic>. 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.</jats:p>
Rodriguez-Martinez A, Ayala R, Posma JM, et al., 2019, pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra, Bioinformatics, Vol: 35, Pages: 1916-1922, ISSN: 1367-4803
Motivation: Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA ("pJRES Binning Algorithm"), which aims to extend the applicability of SRV to pJRES spectra. Results: The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building. Availability: The algorithm is implemented using the MWASTools R/Bioconductor package. Supplementary information: Supplementary data are available at Bioinformatics online.
Neves AL, Rodriguez-Martinez A, Ayala R, et al., 2019, A network-based data-mining approach to investigate indole-related microbiota-host co-metabolism, Publisher: Cold Spring Harbor Laboratory
<jats:title>Abstract</jats:title><jats:sec><jats:title>Motivation</jats:title><jats:p>Indoles have been shown to play a significant role in cardiometabolic disorders. While some individual bacterial species are known to produce indole-adducts, to our best knowledge no studies have made use of publicly available genome data to identify prokaryotes, specifically those associated with the human gut microbiota, contributing to the indole metabolic network.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Here, we propose a computational strategy, comprising the integration of KEGG and BLAST, to identify prokaryote-specific metabolic reactions relevant for the production of indoles, as well as to predict new members of the human gut microbiota potentially involved in these reactions. By identifying relevant prokaryotic species for further validation studies <jats:italic>in vitro</jats:italic>, this strategy represents a useful approach for those interrogating the metabolism of other gut-derived microbial metabolites relevant to human health.</jats:p></jats:sec><jats:sec><jats:title>Availability</jats:title><jats:p>All R scripts and files (gut microbial dataset, FASTA protein sequences, BLASTP output files) are available from <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/AndreaRMICL/Microbial_networks">https://github.com/AndreaRMICL/Microbial_networks</jats:ext-link>.</jats:p></jats:sec><jats:sec><jats:title>Contact</jats:title><jats:p>ARM: <jats:email>firstname.lastname@example.org</jats:email>; LH: <jats:email>email@example.com</jats:email>.</jats:p></jats:sec>
Boulange CL, Rood IM, Posma JM, et al., 2019, NMR and MS urinary metabolic phenotyping in kidney diseases is fit-for-purpose in the presence of a protease inhibitor, Molecular Omics, Vol: 15, Pages: 39-49, ISSN: 2515-4184
Nephrotic syndrome with idiopathic membranous nephropathy as a major contributor, is characterized by proteinuria, hypoalbuminemia and oedema. Diagnosis is based on renal biopsy and the condition is treated using immunosuppressive drugs; however nephrotic syndrome treatment efficacy varies among patients. Multi-omic urine analyses can discover new markers of nephrotic syndrome that can be used to develop personalized treatments. For proteomics, a protease inhibitor (PI) is sometimes added at sample collection to conserve proteins but its impact on urine metabolic phenotyping needs to be evaluated. Urine from controls (n = 4) and idiopathic membranous nephropathy (iMN) patients (n = 6) were collected with and without PI addition and analysed using 1H NMR spectroscopy and UPLC-MS. PI-related data features were observed in the 1H NMR spectra but their removal followed by a median fold change normalisation, eliminated the PI contribution. PI-related metabolites in UPLC-MS data had limited effect on metabolic patterns specific to iMN. When using an appropriate data processing pipeline, PI-containing urine samples are appropriate for 1H NMR and MS metabolic profiling of patients with nephrotic syndrome.
Chan Q, Lau C-HE, Gibson R, et al., 2019, Relationships of Dietary and Supplement Magnesium Intake and Its Urinary Metabolomic Biomarkers With Blood Pressure: The INTERMAP Study, Scientific Sessions of the American-Heart-Association on Epidemiology and Prevention/Lifestyle and Cardiometabolic Health, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322
Posma JM, 2018, Metabolic profiling, Encyclopedia of Bioinformatics and Computational Biology, Editors: Cannataro, Gaeta, Gribskov, Asif Khan, Nakai, Ranganathan, Schonbach
Metabolic profiling involves the measurement of metabolites, small molecules under 1 kDa in weight, in biological fluids such as plasma/serum and urine or in tissue samples using high-throughput spectroscopic and spectrometric technologies. It contributes phenotypic information which cannot be directly obtained from other omic platforms that can be associated with disease diagnosis and prognosis, and provide mechanistic understanding of disease aetiology and drug treatments. This Chapter provides background on the data generated by Nuclear Magnetic Resonance spectroscopy and hyphenated Mass Spectrometry techniques, bioinformatic and statistical methods used to analyse these data and discusses bottlenecks in data processing, analysis and interpretation.
Posma JM, 2018, Multivariate statistical methods for metabolic phenotyping, The Handbook of Metabolic Phenotyping, Editors: Lindon, Holmes, Nicholson, Publisher: Elsevier
The nature of metabolic phenotyping data, where typically more variables are measured than samples are available, requires careful application of statistical methods in order to be able to make meaningful inferences from the data. This Chapter describes different aspects of the multivariate modelling of this type of data, including data transformations and partitioning, unsupervised algorithms for dimension reduction, supervised algorithms for classification, clustering and regression, metrics and methods for obtaining unbiased prediction error estimates of predictive models and statistical spectroscopy tools used for biomarker identification. It focusses on describing methods routinely applied in the field as well as discussing methods that, as computational advancements are made, are poised to become more widely applied to metabolic phenotyping data.
Rodriguez-Martinez A, Ayala R, Posma J, et al., 2018, Exploring the Genetic Landscape of Metabolic Phenotypes with MetaboSignal, Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.], ISSN: 1934-3396
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.
Garcia-Perez I, Posma JM, Gibson R, et al., 2017, Modernazing dietary assessment by use of metabolic profiling, Endocrine Abstracts
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.
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/
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.
Garcia Perez I, Posma JM, Gibson R, et al., 2017, Objective assessment of dietary patterns using metabolic phenotyping: a randomized, controlled, crossover trial, The Lancet Diabetes & Endocrinology, Vol: 5, Pages: 184-195, ISSN: 2213-8587
Background: The burden of non-communicable diseases, such as obesity, diabetes, coronary heart disease and cancer, can be reduced by the consumption of healthy diets. Accurate monitoring of changes in dietary patterns in response to food policy implementation is challenging. Metabolic profiling allows simultaneous measurement of hundreds of metabolites in urine, many of them influenced by food intake. We aim to classify people according to dietary behaviour and enhance dietary reporting using metabolic profiling of urine.Methods: To develop metabolite models from 19 healthy volunteers who attended a clinical research unit for four day periods on four occasions. We used the World Health Organisation’s healthy eating guidelines (increase fruits, vegetables, wholegrains, dietary fibre and decrease fats, sugars, and salt) to develop four dietary interventions lasting for four days each that ranged from a diet associated with a low to high risk of developing non-communicable disease. Urine samples were measured by 1H-NMR spectroscopy. This study is registered as an International Standard Randomized Controlled Trial, number ISRCTN 43087333. INTERMAP U.K. (n=225) and a healthy-eating Danish cohort (n=66) were used as free-living validation datasets.Findings: There was clear separation between the urinary metabolite profiles of the four diets. We also demonstrated significant stepwise differences in metabolite levels between the lowest and highest metabolic risk diets and developed metabolite models for each diet. Application of the derived metabolite models to independent cohorts confirmed the association between urinary metabolic and dietary profiles in INTERMAP (P<0•001) and the Danish cohort (P<0•001).Interpretation: Urinary metabolite models, developed in a highly controlled environment, can classify groups of free-living people into consumers of dietary profiles associated with lower or higher non-communicable disease risk based on multivariate m
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.
Seijger CGW, Drost G, Posma JM, et al., 2016, Overweight Is an Independent Risk Factor for Reduced Lung Volumes in Myotonic Dystrophy Type 1, PLOS One, Vol: 11, ISSN: 1932-6203
Garcia-Perez I, Posma JM, Chambers ES, et al., 2016, An analytical pipeline for quantitative characterization of dietary intake: application to assess grape intake., Journal of Agricultural and Food Chemistry, Vol: 64, Pages: 2423-2431, ISSN: 1520-5118
Lack of accurate dietary assessment in free-living populations requires discovery of new biomarkers reflecting food intake qualitatively and quantitatively to objectively evaluate effects of diet on health. We provide a proof-of-principle for an analytical pipeline to identify quantitative dietary biomarkers. Tartaric acid was identified as dose-responsive urinary biomarker of grape intake and subsequently quantified in volunteers who followed a series of 4-day dietary interventions incorporating 0g/day, 50g/day, 100g/day and 150g/day of grapes in standardized diets from a randomized controlled clinical trial. The most accurate quantitative prediction of grape intake was obtained in 24h urine samples which have the strongest linear relationship between grape intake and tartaric acid excretion (r2=0.90). This new methodological pipeline for estimating nutritional intake based on coupling dietary intake information and quantified nutritional biomarkers was developed and validated in a controlled dietary intervention study, showing that this approach can improve the accuracy of estimating nutritional intakes.
Li J, Kinross JM, Posma JP, et al., 2015, COLONIC MICROBIOME-METABONOME NETWORK INTERACTIONS IN AFRICAN AMERICANS AND NATIVE AFRICANS: A PROSPECTIVE 2-WEEK FOOD EXCHANGE STUDY, 2nd Digestive-Disorders-Federation Conference, Publisher: BMJ PUBLISHING GROUP, Pages: A369-A369, ISSN: 0017-5749
Obesity is a major public health problem worldwide. We used 24-hour urinary metabolic profiling by proton (1H) nuclear magnetic resonance (NMR) spectroscopy and ion exchange chromatography to characterize the metabolic signatures of adiposity in the U.S. (n = 1880) and UK (n = 444) cohorts of the INTERMAP (International Study of Macro- and Micronutrients and Blood Pressure) epidemiologic study. Metabolic profiling of urine samples collected over two 24-hour time periods 3 weeks apart showed reproducible patterns of metabolite excretion associated with adiposity. Exploratory analysis of the urinary metabolome using 1H NMR spectroscopy of the U.S. samples identified 29 molecular species, clustered in interconnecting metabolic pathways, that were significantly associated (P = 1.5 × 10−5 to 2.0 × 10−36) with body mass index (BMI); 25 of these species were also found in the UK validation cohort. We found multiple associations between urinary metabolites and BMI including urinary glycoproteins and N-acetyl neuraminate (related to renal function), trimethylamine, dimethylamine, 4-cresyl sulfate, phenylacetylglutamine and 2-hydroxyisobutyrate (gut microbial co-metabolites), succinate and citrate (tricarboxylic acid cycle intermediates), ketoleucine and the ketoleucine/leucine ratio (linked to skeletal muscle mitochondria and branched-chain amino acid metabolism), ethanolamine (skeletal muscle turnover), and 3-methylhistidine (skeletal muscle turnover and meat intake). We mapped the multiple BMI-metabolite relationships as part of an integrated systems network that describes the connectivities between the complex pathway and compartmental signatures of human adiposity.
O'Keefe SJ, Li JV, Lahti L, et al., 2015, Fat, fibre and cancer risk in African Americans and rural Africans., Nat Commun, Vol: 6
Rates of colon cancer are much higher in African Americans (65:100,000) than in rural South Africans (<5:100,000). The higher rates are associated with higher animal protein and fat, and lower fibre consumption, higher colonic secondary bile acids, lower colonic short-chain fatty acid quantities and higher mucosal proliferative biomarkers of cancer risk in otherwise healthy middle-aged volunteers. Here we investigate further the role of fat and fibre in this association. We performed 2-week food exchanges in subjects from the same populations, where African Americans were fed a high-fibre, low-fat African-style diet and rural Africans a high-fat, low-fibre western-style diet, under close supervision. In comparison with their usual diets, the food changes resulted in remarkable reciprocal changes in mucosal biomarkers of cancer risk and in aspects of the microbiota and metabolome known to affect cancer risk, best illustrated by increased saccharolytic fermentation and butyrogenesis, and suppressed secondary bile acid synthesis in the African Americans.
Lamour SD, Veselkov KA, Posma JM, et al., 2014, Metabolic, Immune, and Gut Microbial Signals Mount a Systems Response to Leishmania major Infection, Journal of Proteome Research, Vol: 14, Pages: 318-329, ISSN: 1535-3907
Parasitic infections such as leishmaniasis induce a cascade of host physiological responses, includingmetabolic and immunological changes. Infection with Leishmania major parasites causes cutaneousleishmaniasis in humans, a neglected tropical disease that is difficult to manage. To understand thedeterminants of pathology, we studied L. major infection in two mouse models: the self-healingC57BL/6 strain and the nonhealing BALB/c strain. Metabolic profiling of urine, plasma, and feces viaproton NMR spectroscopy was performed to discover parasite-specific imprints on global hostmetabolism. Plasma cytokine status and fecal microbiome were also characterized as additional metricsof the host response to infection. Results demonstrated differences in glucose and lipid metabolism,distinctive immunological phenotypes, and shifts in microbial composition between the two models.We present a novel approach to integrate such metrics using correlation network analyses, wherebyself-healing mice demonstrated an orchestrated interaction between the biological measures shortlyafter infection. In contrast, the response observed in nonhealing mice was delayed and fragmented. Ourstudy suggests that trans-system communication across host metabolism, the innate immune system,and gut microbiome is key for a successful host response to L. major and provides a new concept,potentially translatable to other diseases.
Villaseñor A, Garcia-Perez I, García A, et al., 2014, Breast milk metabolome characterization in a single phase extrac-tion, multiplatform analytical approach, Analytical Chemistry, Vol: 86, Pages: 8245-8252, ISSN: 0003-2700
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