Publications
118 results found
Robinson O, Chadeau Hyam M, Karaman I, et al., 2020, Determinants of accelerated metabolomic and epigenetic ageing in a UK cohort, Aging Cell, Vol: 19, Pages: 1-13, ISSN: 1474-9718
Markers of biological aging have potential utility in primary care and public health. We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography–mass spectrometry in urine and serum, within a large sample (N = 2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. We investigated the determinants of accelerated aging, including lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (mean r = .86 across independent test sets). Increased metabolomic age acceleration (mAA) was associated after false discovery rate (FDR) correction with overweight/obesity, diabetes, heavy alcohol use and depression. DNA methylation age acceleration measures were uncorrelated with mAA. Increased DNA methylation phenotypic age acceleration (N = 1,110) was associated after FDR correction with heavy alcohol use, hypertension and low income. In conclusion, metabolomics is a promising approach for the assessment of biological age and appears complementary to established epigenetic clocks.
Gadgil MD, Sands C, Lewis MR, et al., 2020, Circulating Metabolites Are Associated with Glycemic Measures in South Asians, DIABETES, Vol: 69, ISSN: 0012-1797
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Takis P, Jimenez B, Sands C, et al., 2020, SMolESY: An efficient and quantitative alternative to on-instrument macromolecular ¹H-NMR signal suppression, Chemical Science, Vol: 11, Pages: 6000-6011, ISSN: 2041-6520
One-dimensional (1D) proton-nuclear magnetic resonance (1H-NMR) spectroscopy is an established technique for measuring small molecules in a wide variety of complex biological sample types. It is demonstrably reproducible, easily automatable and consequently ideal for routine and large-scale application. However, samples containing proteins, lipids, polysaccharides and other macromolecules produce broad signals which overlap and convolute those from small molecules. NMR experiment types designed to suppress macromolecular signals during acquisition may be additionally performed, however these approaches add to the overall sample analysis time and cost, especially for large cohort studies, and fail to produce reliably quantitative data. Here, we propose an alternative way of computationally eliminating macromolecular signals, employing the mathematical differentiation of standard 1H-NMR spectra, producing small molecule-enhanced spectra with preserved quantitative capability and increased resolution. Our approach, presented in its simplest form, was implemented in a cheminformatic toolbox and successfully applied to more than 3000 samples of various biological matrices rich or potentially rich with macromolecules, offering an efficient alternative to on-instrument experimentation, facilitating NMR use in routine and large-scale applications.
Andreas NJ, Roy RB, Gomez-Romero M, et al., 2020, Performance of metabonomic serum analysis for diagnostics in paediatric tuberculosis, Scientific Reports, Vol: 10, Pages: 1-11, ISSN: 2045-2322
We applied a metabonomic strategy to identify host biomarkers in serum to diagnose paediatric tuberculosis (TB) disease. 112 symptomatic children with presumptive TB were recruited in The Gambia and classified as bacteriologically-confirmed TB, clinically diagnosed TB, or other diseases. Sera were analysed using 1H nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). Multivariate data analysis was used to distinguish patients with TB from other diseases. Diagnostic accuracy was evaluated using Receiver Operating Characteristic (ROC) curves. Model performance was tested in a validation cohort of 36 children from the UK. Data acquired using 1H NMR demonstrated a sensitivity, specificity and Area Under the Curve (AUC) of 69% (95% confidence interval [CI], 56–73%), 83% (95% CI, 73–93%), and 0.78 respectively, and correctly classified 20% of the validation cohort from the UK. The most discriminatory MS data showed a sensitivity of 67% (95% CI, 60–71%), specificity of 86% (95% CI, 75–93%) and an AUC of 0.78, correctly classifying 83% of the validation cohort. Amongst children with presumptive TB, metabolic profiling of sera distinguished bacteriologically-confirmed and clinical TB from other diseases. This novel approach yielded a diagnostic performance for paediatric TB comparable to that of Xpert MTB/RIF and interferon gamma release assays.
Maciejewski M, Sands C, Nair N, et al., 2020, PREDICTION OF RESPONSE OF METHOTREXATE IN PATIENTS WITH RHEUMATOID ARTHRITIS USING SERUM LIPIDOMICS, Annual Conference of the British-Society-for-Rheumatology (BSR), Publisher: OXFORD UNIV PRESS, ISSN: 1462-0324
Sands C, Wolfer A, DS Correia G, et al., 2019, The nPYc-Toolbox, a Python module for the pre-processing, quality-control, and analysis of metabolic profiling datasets, Bioinformatics, Vol: 35, Pages: 5359-5360, ISSN: 1367-4803
Summary: As large-scale metabolic phenotyping studies become increasingly common, the need forsystemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysishas become increasingly important, both within a study, and to allow meaningful inter-study comparisons.The nPYc-Toolbox provides software for the import, pre-processing, QC, and visualisation of metabolicphenotyping datasets, either interactively, or in automated pipelines.Availability and Implementation: The nPYc-Toolbox is implemented in Python, and is freelyavailable from the Python package index https://pypi.org/project/nPYc/, source isavailable at https://github.com/phenomecentre/nPYc-Toolbox. Full documentation canbe found at http://npyc-toolbox.readthedocs.io/ and exemplar datasets and tutorials athttps://github.com/phenomecentre/nPYc-toolbox-tutorials
Wang X, Nijman R, Camuzeaux S, et al., 2019, Plasma lipid profiles discriminate bacterial from viral infection in febrile children, Scientific Reports, Vol: 9, ISSN: 2045-2322
Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The ‘omics’ approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n=20) and confirmed viral infection (n=20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group..A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics.
Whiley L, Chekmeneva E, Berry DJ, et al., 2019, Systematic isolation and structure elucidation of urinary metabolites optimized for the analytical-scale molecular profiling laboratory, Analytical Chemistry, Vol: 91, Pages: 8873-8882, ISSN: 0003-2700
Annotation and identification of metabolite biomarkers is critical for their biological interpretation in metabolic phenotyping studies, presenting a significant bottleneck in the successful implementation of untargeted metabolomics. Here, a systematic multi-step protocol was developed for the purification and de novo structural elucidation of urinary metabolites. The protocol is most suited for instances where structure elucidation and metabolite annotation are critical for the downstream biological interpretation of metabolic phenotyping studies. First, a bulk urine pool was desalted using ion-exchange resins enabling large-scale fractionation using precise iterations of analytical scale chromatography. Primary urine fractions were collected and assembled into a “fraction bank” suitable for long-term laboratory storage. Secondary and tertiary fractionations exploited differences in selectivity across a range of reversed-phase chemistries, achieving the purification of metabolites of interest yielding an amount of material suitable for chemical characterisation. To exemplify the application of the systematic workflow in a diverse set of cases, four metabolites with a range of physico-chemical properties were selected and purified from urine and subjected to chemical formula and structure elucidation by respective magnetic resonance mass spectrometry (MRMS) and NMR analyses. Their structures were fully assigned as teterahydropentoxyline, indole-3-acetic-acid-O-glucuronide, p-cresol glucuronide, and pregnanediol-3-glucuronide. Unused effluent was collected, dried and returned to the fraction bank, demonstrating the viability of the system for repeat use in metabolite annotation with a high degree of efficiency.
Gafson AR, Savva C, Thorne T, et al., 2019, Breaking the cycle: reversal of flux in the tricarboxylic acid cycle by dimethyl fumarate, Neurology, Neuroimmunology and Neuroinflammation, Vol: 6, ISSN: 2332-7812
ObjectiveTo infer possible molecular effectors of therapeutic effects and adverse events for the pro-drug dimethyl fumarate (DMF, Tecfidera) in the plasma of relapsing-remitting MS patients (RRMS) based on untargeted blood plasma metabolomics. MethodsBlood samples were collected from 27 RRMS patients at baseline and six weeks after initiation of treatment with DMF (BG-12; Tecfidera). Patients were separated into a discovery (n=15) and a validation cohort (n=12). Ten healthy controls were also recruited and blood samples were collected over the same time intervals. Untargeted metabolomic profiling using ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS) was performed on plasma samples from the discovery cohort and healthy controls at Metabolon Inc. (Durham, NC). UPLC-MS was then performed on samples from the validation cohort at the National Phenome Centre (Imperial College, UK). Plasma neurofilament concentration (NfL) was also assayed for all subjects using the Simoa platform (Quanterix, Lexington, MA). Time course and cross-sectional statistical analyses were performed to identify pharmacodynamic changes in the metabolome secondary to DMF and relate these to adverse events. Results In the discovery cohort, tricarboxylic acid (TCA) cycle intermediates fumarate and succinate and TCA cycle metabolites succinyl-carnitine and methyl succinyl-carnitine were increased 6-weeks after the start of treatment (q < 0.05). We confirmed that methyl succinyl carnitine was also increased in the validation cohort 6-weeks after the start of treatment (q < 0.05). Changes in concentrations of these metabolites were not seen over a similar time period in blood from the untreated healthy control population. Increased succinyl-carnitine and methyl succinyl-carnitine were associated with adverse events from DMF (flushing, abdominal symptoms. The mean plasma NfL concentration before treatment was higher in the RRMS patients than in the healthy contro
Whiley LW, Nye L, Grant I, et al., 2019, Ultrahigh-performance liquid chromatography tandem mass spectrometry with electrospray ionization quantification of tryptophan metabolites and markers of gut health in serum and plasmaapplication to clinical and epidemiology cohorts, Analytical Chemistry, Vol: 91, Pages: 5207-5216, ISSN: 0003-2700
A targeted ultrahigh-performance liquid chromatography tandem mass spectrometry with electrospray ionization (UHPLC-ESI-MS/MS) method has been developed for the quantification of tryptophan and its downstream metabolites from the kynurenine and serotonin pathways. The assay coverage also includes markers of gut health and inflammation, including citrulline and neopterin. The method was designed in 96-well plate format for application in multiday, multiplate clinical and epidemiology population studies. A chromatographic cycle time of 7 min enables the analysis of two 96-well plates in 24 h. To protect chromatographic column lifespan, samples underwent a two-step extraction, using solvent protein precipitation followed by delipidation via solid-phase extraction (SPE). Analytical validation reported accuracy of each analyte <20% for the lowest limit of quantification and <15% for all other quality control (QC) levels. The analytical precision for each analyte was 2.1–12.9%. To test the applicability of the method to multiplate and multiday preparations, a serum pool underwent periodic repeat analysis during a run consisting of 18 plates. The % CV (coefficient of variation) values obtained for each analyte were <15%. Additional biological testing applied the assay to samples collected from healthy control participants and two groups diagnosed with inflammatory bowel disease (IBD) (one group treated with the anti-inflammatory 5-aminosalicylic acid (5-ASA) and one group untreated), with results showing significant differences in the concentrations of picolinic acid, kynurenine, and xanthurenic acid. The short analysis time and 96-well plate format of the assay makes it suitable for high-throughput targeted UHPLC-ESI-MS/MS metabolomic analysis in large-scale clinical and epidemiological population studies.
Wolfer K, Lewis M, Sarafian M, et al., 2019, Improved stratification of liver failure syndromes using broad-panel bile acid LCMS phenotyping demonstrates novel pathways of dysregulation in tertiary bile acids in acute-on-chronic liver failure, International Liver Congress / 54th Annual Meeting of the European-Association-for-the-Study-of-the-Liver (EASL), Publisher: ELSEVIER, Pages: E184-E185, ISSN: 0168-8278
Dunn WB, Lewis MR, 2019, The Role of Ultra Performance Liquid Chromatography-Mass Spectrometry in Metabolic Phenotyping, HANDBOOK OF METABOLIC PHENOTYPING, Editors: Lindon, Nicholson, Holmes, Publisher: ELSEVIER SCIENCE BV, Pages: 97-136, ISBN: 978-0-12-812293-8
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- Citations: 2
Bonner FW, Maslen L, Lindon JC, et al., 2019, Conception, Implementation and Operation of Large-Scale Metabolic Phenotyping Centres: Phenome Centres, HANDBOOK OF METABOLIC PHENOTYPING, Editors: Lindon, Nicholson, Holmes, Publisher: ELSEVIER SCIENCE BV, Pages: 385-405, ISBN: 978-0-12-812293-8
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- Citations: 2
Izzi-Engbeaya CN, Comninos AN, Clarke S, et al., 2018, The effects of kisspeptin on β-cell function, serum metabolites and appetite in humans, Diabetes, Obesity and Metabolism, Vol: 20, Pages: 2800-2810, ISSN: 1462-8902
AimsTo investigate the effect of kisspeptin on glucose‐stimulated insulin secretion and appetite in humans.Materials and methodsIn 15 healthy men (age: 25.2 ± 1.1 years; BMI: 22.3 ± 0.5 kg m−2), we compared the effects of 1 nmol kg−1 h−1 kisspeptin versus vehicle administration on glucose‐stimulated insulin secretion, metabolites, gut hormones, appetite and food intake. In addition, we assessed the effect of kisspeptin on glucose‐stimulated insulin secretion in vitro in human pancreatic islets and a human β‐cell line (EndoC‐βH1 cells).ResultsKisspeptin administration to healthy men enhanced insulin secretion following an intravenous glucose load, and modulated serum metabolites. In keeping with this, kisspeptin increased glucose‐stimulated insulin secretion from human islets and a human pancreatic cell line in vitro. In addition, kisspeptin administration did not alter gut hormones, appetite or food intake in healthy men.ConclusionsCollectively, these data demonstrate for the first time a beneficial role for kisspeptin in insulin secretion in humans in vivo. This has important implications for our understanding of the links between reproduction and metabolism in humans, as well as for the ongoing translational development of kisspeptin‐based therapies for reproductive and potentially metabolic conditions.
Jimenez B, Holmes E, Heude C, et al., 2018, Quantitative lipoprotein subclass and low molecular weight metabolite analysis in human serum and plasma by 1H NMR spectroscopy in a multilaboratory trial, Analytical Chemistry, Vol: 90, Pages: 11962-11971, ISSN: 0003-2700
We report an extensive 600 MHz NMR trial of a quantitative lipoprotein and small molecule measurements in human blood serum and plasma. Five centers with eleven 600 MHz NMR spectrometers were used to analyze 98 samples including: 20 QCs, 37 commercially sourced, paired serum and plasma samples and 2 National Institute of Science and Technology, NIST, reference material 1951c replicates. Samples were analyzed using rigorous protocols for sample preparation and experimental acquisition. A commercial lipoprotein subclass analysis was used to quantify 105 lipoprotein subclasses and 24 low molecular weight metabolites from the nuclear magnetic resonance, NMR, spectra. For all spectrometers, the instrument specific variance in measuring internal quality controls, QCs, was lower than the percentage described by the National Cholesterol Education Program, NCEP, criteria for lipid testing (triglycerides<2.7%, cholesterol<2.8%; LDL-cholesterol<2.8%; HDL-cholesterol<2.3%), showing exceptional reproducibility for direct quantitation of lipoproteins in both matrices. The average RSD for the 105 lipoprotein parameters in the 11 instruments was 4.6% and 3.9% for the two NIST samples while it was 38% and 40% for the 37 commercially sourced plasmas and sera, respectively, showing negligible analytical compared to biological variation. The coefficient of variance, CV, obtained for the quantification of the small molecules across the 11 spectrometers was below 15% for 20 out of the 24 metabolites analyzed. This study provides further evidence of the suitability of NMR for high-throughput lipoprotein subcomponent analysis and small molecule quantitation with the exceptional reproducibility required for clinical and other regulatory settings.
Domingo-Almenara X, Montenegro-Burke JR, Ivanisevic J, et al., 2018, CMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules, Nature Methods, Vol: 15, Pages: 681-684, ISSN: 1548-7091
We report XCMS-MRM and METLIN-MRM (http://xcmsonline-mrm.scripps.edu/ and http://metlin.scripps.edu/), a cloud-based data-analysis platform and a public multiple-reaction monitoring (MRM) transition repository for small-molecule quantitative tandem mass spectrometry. This platform provides MRM transitions for more than 15,500 molecules and facilitates data sharing across different instruments and laboratories.
Broadhurst D, Goodacre R, Reinke SN, et al., 2018, Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies, Metabolomics, Vol: 14, ISSN: 1573-3882
BackgroundQuality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition.Aim of reviewThis tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported.Key scientific concepts of reviewSystem suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.
van Golen RF, Olthof PB, de Haan LR, et al., 2018, The pathophysiology of human obstructive cholestasis is mimicked in cholestatic Gold Syrian hamsters, BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE, Vol: 1864, Pages: 942-951, ISSN: 0925-4439
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- Citations: 6
Lewis M, 2018, Assay Development Enables Large-Scale Metabolic Profiling, Genetic Engineering and Biotechnology News, Vol: 38, Pages: 18-19, ISSN: 1935-472X
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.
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.
Kaluarachchi M, Lewis MR, Lindon JC, 2017, Standardized Protocols for MS-Based Metabolic Phenotyping, Encyclopedia of Spectroscopy and Spectrometry, Pages: 224-231, ISBN: 9780128032244
Metabolic phenotyping, also known as metabolic profiling, metabonomics, and metabolomics, usually employs NMR spectroscopy and mass spectrometry (MS) as analytic technologies. MS is usually, but not always, preceded by a separation stage, typically gas chromatography (GC), liquid chromatography (HPLC or UPLC), or capillary electrophoresis (CE). A wide variety of separation and MS ionization methods as well as data preprocessing methods and protocols have been employed so far for MS-based metabolic phenotyping, and some attempts have been made at standardization and validation. This article provides a summary of the current situation with regard to standardization methods, conventions, and protocols.
Kaluarachchi M, Lewis MR, Lindon JC, 2017, Standardized Protocols for MS-Based Metabolic Phenotyping, ENCYCLOPEDIA OF SPECTROSCOPY AND SPECTROMETRY, 3RD EDITION, VOL 4: S-Z, Editors: Lindon, Tranter, Koppenaal, Publisher: ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD, Pages: 224-231
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- Citations: 7
Maitre L, Villanueva CM, Lewis M, et al., 2016, Maternal urinary metabolic signatures of fetal growth and associated clinical and environmental factors in the INMA study, BMC Medicine, Vol: 14, ISSN: 1741-7015
BackgroundMaternal metabolism during pregnancy is a major determinant of the intra-uterine environment and fetal outcomes. Herein, we characterize the maternal urinary metabolome throughout pregnancy to identify maternal metabolic signatures of fetal growth in two subcohorts and explain potential sources of variation in metabolic profiles based on lifestyle and clinical data.MethodsWe used 1H nuclear magnetic resonance (NMR) spectroscopy to characterize maternal urine samples collected in the INMA birth cohort at the first (n = 412 and n = 394, respectively, in Gipuzkoa and Sabadell cohorts) and third trimesters of gestation (n = 417 and 469). Metabolic phenotypes that reflected longitudinal intra- and inter-individual variation were used to predict measures of fetal growth and birth weight.ResultsA metabolic shift between the first and third trimesters of gestation was characterized by 1H NMR signals arising predominantly from steroid by-products. We identified 10 significant and reproducible metabolic associations in the third trimester with estimated fetal, birth, and placental weight in two independent subcohorts. These included branched-chain amino acids; isoleucine, valine, leucine, alanine and 3 hydroxyisobutyrate (metabolite of valine), which were associated with a significant fetal weight increase at week 34 of up to 2.4 % in Gipuzkoa (P < 0.005) and 1 % in Sabadell (P < 0.05). Other metabolites included pregnancy-related hormone by-products of estrogens and progesterone, and the methyl donor choline. We could explain a total of 48–53 % of the total variance in birth weight of which urine metabolites had an independent predictive power of 12 % adjusting for all other lifestyle/clinical factors. First trimester metabolic phenotypes could not predict reproducibly weight at later stages of development. Physical activity, as well as other modifiable lifestyle/clinical factors, suc
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
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.
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
McPhail MJW, Shawcross D, Lewis MR, et al., 2016, Mutlivariate metabotyping of plasma accurately predicts survival in decompensated cirrhosis, Journal of Hepatology, Vol: 64, Pages: 1058-1067, ISSN: 1600-0641
Background & AimsPredicting survival in decompensated cirrhosis (DC) is important in decision making for liver transplantation and resource allocation. We investigated whether high-resolution metabolic profiling can determine a metabolic phenotype associated with 90-day survival.MethodsTwo hundred and forty-eight subjects underwent plasma metabotyping by 1H nuclear magnetic resonance (NMR) spectroscopy and reversed-phase ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry (UPLC-TOF-MS; DC: 80-derivation set, 101-validation; stable cirrhosis (CLD) 20 and 47 healthy controls (HC)).Results1H NMR metabotyping accurately discriminated between surviving and non-surviving patients with DC. The NMR plasma profiles of non-survivors were attributed to reduced phosphatidylcholines and lipid resonances, with increased lactate, tyrosine, methionine and phenylalanine signal intensities. This was confirmed on external validation (area under the receiver operating curve [AUROC] = 0.96 (95% CI 0.90–1.00, sensitivity 98%, specificity 89%). UPLC-TOF-MS confirmed that lysophosphatidylcholines and phosphatidylcholines [LPC/PC] were downregulated in non-survivors (UPLC-TOF-MS profiles AUROC of 0.94 (95% CI 0.89–0.98, sensitivity 100%, specificity 85% [positive ion detection])). LPC concentrations negatively correlated with circulating markers of cell death (M30 and M65) levels in DC. Histological examination of liver tissue from DC patients confirmed increased hepatocyte cell death compared to controls. Cross liver sampling at time of liver transplantation demonstrated that hepatic endothelial beds are a source of increased circulating total cytokeratin-18 in DC.ConclusionPlasma metabotyping accurately predicts mortality in DC. LPC and amino acid dysregulation is associated with increased mortality and severity of disease reflecting hepatocyte cell death.
Ghataorhe P, Rhodes C, Wharton J, et al., 2016, Metabonomic Phenotype Analysis In Patients With Pulmonary Hypertension, International Conference of the American-Thoracic-Society (ATS), Publisher: AMER THORACIC SOC, ISSN: 1073-449X
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