Imperial College London

Dr Matthew R. Lewis

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Director of Metabolic Profiling - COO NPC
 
 
 
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Contact

 

matthew.lewis

 
 
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Location

 

660Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

70 results found

Jones B, Sands C, Alexiadou K, Minnion J, Tharakan G, Behary P, Ahmed A, Purkayastha S, Lewis M, Bloom S, Li J, Tan Tet al., 2021, The metabolomic effects of tripeptide gut hormone infusion compared to Roux-en-Y gastric bypass and caloric restriction, Journal of Clinical Endocrinology and Metabolism, ISSN: 0021-972X

Context: The gut-derived peptide hormones glucagon-like peptide-1 (GLP-1), oxyntomodulin (OXM), and peptide YY (PYY) are regulators of energy intake and glucose homeostasis, and are thought to contribute to the glucose-lowering effects of bariatric surgery. Objective: To establish the metabolomic effects of a combined infusion of GLP-1, OXM and PYY (tripeptide “GOP”) in comparison to a placebo infusion, Roux-en-Y gastric bypass (RYGB) surgery, and a very low-calorie diet (VLCD). Design and setting: Sub-analysis of a single-blind, randomised, placebo-controlled study of GOP infusion (ClinicalTrials.gov NCT01945840), including VLCD and RYGB comparator groups. Patients and interventions: 25 obese patients with type 2 diabetes or prediabetes were randomly allocated to receive a 4-week subcutaneous infusion of GOP (n=14) or 0.9% saline control (SAL; n=11). An additional 22 patients followed a VLCD, and 21 underwent RYGB surgery. Main outcome measures: Plasma and urine samples collected at baseline and 4 weeks into each intervention were subjected to cross-platform metabolomic analysis, followed by unsupervised and supervised modelling approaches to identify similarities and differences between the effects of each intervention. Results: Aside from glucose, very few metabolites were affected by GOP, contrasting with major metabolomic changes seen with VLCD and RYGB. Conclusions: Treatment with GOP provides a powerful glucose-lowering effect but does not replicate the broader metabolomic changes seen with VLCD and RYGB. The contribution of these metabolomic changes to the clinical benefits of RYGB remains to be elucidated.

Journal article

Ferreira MR, Sands CJ, Li JV, Andreyev JN, Chekmeneva E, Gulliford S, Marchesi J, Lewis MR, Dearnaley DPet al., 2021, Impact of Pelvic Radiation Therapy for Prostate Cancer on Global Metabolic Profiles and Microbiota-Driven Gastrointestinal Late Side Effects: A Longitudinal Observational Study., Int J Radiat Oncol Biol Phys

PURPOSE: Radiation therapy to the prostate and pelvic lymph nodes (PLNRT) is part of the curative treatment of high-risk prostate cancer. Yet, the broader influence of radiation therapy on patient physiology is poorly understood. We conducted comprehensive global metabolomic profiling of urine, plasma, and stools sampled from patients undergoing PLNRT for high-risk prostate cancer. METHODS AND MATERIALS: Samples were taken from 32 patients at 6 timepoints: baseline, 2 to 3 and 4 to 5 weeks of PLNRT; and 3, 6, and 12 months after PLNRT. We characterized the global metabolome of urine and plasma using 1H nuclear magnetic resonance spectroscopy and ultraperformance liquid chromatography-mass spectrometry, and of stools with nuclear magnetic resonance. Linear mixed-effects modeling was used to investigate metabolic changes between timepoints for each biofluid and assay and determine metabolites of interest. RESULTS: Metabolites in urine, plasma and stools changed significantly after PLNRT initiation. Metabolic profiles did not return to baseline up to 1 year post-PLNRT in any biofluid. Molecules associated with cardiovascular risk were increased in plasma. Pre-PLNRT fecal butyrate levels directly associated with increasing gastrointestinal side effects, as did a sharper fall in those levels during and up to 1 year postradiation therapy, mirroring our previous results with metataxonomics. CONCLUSIONS: We showed for the first time that an overall metabolic effect is observed in patients undergoing PLNRT up to 1 year posttreatment. These metabolic changes may effect on long-term morbidity after treatment, which warrants further investigation.

Journal article

Blaise BJ, Correia GDS, Haggart GA, Surowiec I, Sands C, Lewis MR, Pearce JTM, Trygg J, Nicholson JK, Holmes E, Ebbels TMDet al., 2021, Statistical analysis in metabolic phenotyping, NATURE PROTOCOLS, Vol: 16, Pages: 4299-4326, ISSN: 1754-2189

Journal article

Sharma R, Lu H, George J, Eslam M, Villanueva A, Ward C, Reeves HL, McCain M, Chambers E, Sands C, Maslen L, Lewis M, Ramaswami Ret al., 2021, Discriminatory changes in circulating lipid and small molecule metabolites in patients with MAFLD associated hepatocellular cancer, Publisher: ELSEVIER, Pages: S490-S490, ISSN: 0168-8278

Conference paper

Wolfer AM, Correia GDS, Sands CJ, Camuzeaux S, Yuen AHY, Chekmeneva E, Takáts Z, Pearce JTM, Lewis MRet al., 2021, peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets., Bioinformatics

 : Untargeted LC-MS profiling assays are capable of measuring thousands of chemical compounds in a single sample, but unreliable feature extraction and metabolite identification remain considerable barriers to their interpretation and usefulness. peakPantheR (Peak Picking and ANnoTation of High-resolution Experiments in R) is an R package for the targeted extraction and integration of annotated features from LC-MS profiling experiments. It takes advantage of chromatographic and spectral databases and prior information of sample matrix composition to generate annotated and interpretable metabolic phenotypic datasets and power workflows for real time data quality assessment. AVAILABILITY: peakPantheR is available via Bioconductor (https://bioconductor.org/packages/peakPantheR/). Documentation and worked examples are available at https://phenomecentre.github.io/peakPantheR.github.io/ and https://github.com/phenomecentre/metabotyping-dementia-urine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Journal article

Li J, 2021, Roux-en-Y Gastric bypass-induced bacterial perturbation contributes to altered host-bacterial co-metabolic phenotype, Microbiome, Vol: 9, ISSN: 2049-2618

BACKGROUND: Bariatric surgery, used to achieve effective weight loss in individuals with severe obesity, modifies the gut microbiota and systemic metabolism in both humans and animal models. The aim of the current study was to understand better the metabolic functions of the altered gut microbiome by conducting deep phenotyping of bariatric surgery patients and bacterial culturing to investigate causality of the metabolic observations. METHODS: Three bariatric cohorts (n = 84, n = 14 and n = 9) with patients who had undergone Roux-en-Y gastric bypass (RYGB), sleeve gastrectomy (SG) or laparoscopic gastric banding (LGB), respectively, were enrolled. Metabolic and 16S rRNA bacterial profiles were compared between pre- and post-surgery. Faeces from RYGB patients and bacterial isolates were cultured to experimentally associate the observed metabolic changes in biofluids with the altered gut microbiome. RESULTS: Compared to SG and LGB, RYGB induced the greatest weight loss and most profound metabolic and bacterial changes. RYGB patients showed increased aromatic amino acids-based host-bacterial co-metabolism, resulting in increased urinary excretion of 4-hydroxyphenylacetate, phenylacetylglutamine, 4-cresyl sulphate and indoxyl sulphate, and increased faecal excretion of tyramine and phenylacetate. Bacterial degradation of choline was increased as evidenced by altered urinary trimethylamine-N-oxide and dimethylamine excretion and faecal concentrations of dimethylamine. RYGB patients' bacteria had a greater capacity to produce tyramine from tyrosine, phenylalanine to phenylacetate and tryptophan to indole and tryptamine, compared to the microbiota from non-surgery, normal weight individuals. 3-Hydroxydicarboxylic acid metabolism and urinary excretion of primary bile acids, serum BCAAs and dimethyl sulfone were also perturbed following bariatric surgery. CONCLUSION: Altered bacterial composition and metabolism contribute to metabolic observations in biofluid

Journal article

Maciejewski M, Sands C, Nair N, Ling S, Verstappen S, Hyrich K, Barton A, Ziemek D, Lewis MR, Plant Det al., 2021, Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics, Scientific Reports, Vol: 11, ISSN: 2045-2322

Methotrexate (MTX) is a common first-line treatment for new-onset rheumatoid arthritis (RA). However, MTX is ineffective for 30-40% of patients and there is no way to know which patients might benefit. Here, we built statistical models based on serum lipid levels measured at two time-points (pre-treatment and following 4 weeks on-drug) to investigate if MTX response (by 6 months) could be predicted. Patients about to commence MTX treatment for the first time were selected from the Rheumatoid Arthritis Medication Study (RAMS). Patients were categorised as good or non-responders following 6 months on-drug using EULAR response criteria. Serum lipids were measured using ultra-performance liquid chromatography-mass spectrometry and supervised machine learning methods (including regularized regression, support vector machine and random forest) were used to predict EULAR response. Models including lipid levels were compared to models including clinical covariates alone. The best performing classifier including lipid levels (assessed at 4 weeks) was constructed using regularized regression (ROC AUC 0.61 ± 0.02). However, the clinical covariate based model outperformed the classifier including lipid levels when either pre- or on-treatment time-points were investigated (ROC AUC 0.68 ± 0.02). Pre- or early-treatment serum lipid profiles are unlikely to inform classification of MTX response by 6 months with performance adequate for use in RA clinical management.

Journal article

Takis PG, Jiménez B, Al-Saffar NMS, Harvey N, Chekmeneva E, Misra S, Lewis MRet al., 2021, A computationally lightweight algorithm for deriving reliable metabolite panel measurements from 1D 1H NMR., Analytical Chemistry, Vol: 93, Pages: 4995-5000, ISSN: 0003-2700

Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of 1H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional 1H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications.

Journal article

Sands CJ, Gómez-Romero M, Correia G, Chekmeneva E, Camuzeaux S, Izzi-Engbeaya C, Dhillo WS, Takats Z, Lewis MRet al., 2021, Representing the metabolome with high fidelity: range and response as quality control factors in LC-MS-based global profiling., Analytical Chemistry, Vol: 93, Pages: 1924-1933, ISSN: 0003-2700

Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.

Journal article

de Haan LR, Verheij J, van Golen RF, Horneffer-van der Sluis V, Lewis MR, Beuers UHW, van Gulik TM, Olde Damink SWM, Schaap FG, Heger M, Olthof PBet al., 2021, Unaltered Liver Regeneration in Post-Cholestatic Rats Treated with the FXR Agonist Obeticholic Acid, BIOMOLECULES, Vol: 11

Journal article

Letertre MPM, Myridakis A, Whiley L, Camuzeaux S, Lewis MR, Chappell KE, Thaikkatil A, Dumas M-E, Nicholson JK, Swann JR, Wilson IDet al., 2021, A targeted ultra performance liquid chromatography - Tandem mass spectrometric assay for tyrosine and metabolites in urine and plasma: Application to the effects of antibiotics on mice, JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES, Vol: 1164, ISSN: 1570-0232

Journal article

Whiley L, Chappell KE, D'Hondt E, Lewis MR, Jimenez B, Snowden SG, Soininen H, Kloszewska I, Mecocci P, Tsolaki M, Vellas B, Swann JR, Hye A, Lovestone S, Legido-Quigley C, Holmes Eet al., 2021, Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer's disease, Alzheimers Research & Therapy, Vol: 13, Pages: 1-18, ISSN: 1758-9193

BackgroundBoth serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer’s disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls.MethodsMetabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations.ResultsResults revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced o

Journal article

Gadgil MD, Kanaya AM, Sands C, Lewis MR, Kandula NR, Herrington DMet al., 2020, Circulating metabolites and lipids are associated with glycaemic measures in South Asians, DIABETIC MEDICINE, Vol: 38, ISSN: 0742-3071

Journal article

Kurbatova N, Garg M, Whiley L, Chekmeneva E, Jimenez B, Gomez-Romero M, Pearce J, Kimhofer T, D'Hondt E, Soininen H, Kloszewska I, Mecocci P, Tsolaki M, Vellas B, Aarsland D, Nevado-Holgado A, Liu B, Snowden S, Proitsi P, Ashton NJ, Hye A, Legido-Quigley C, Lewis MR, Nicholson JK, Holmes E, Brazma A, Lovestone Set al., 2020, Urinary metabolic phenotyping for Alzheimer's disease, Scientific Reports, Vol: 10, ISSN: 2045-2322

Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.

Journal article

Ferreira MR, Sands CJ, Li JV, Andreyev HJN, Marchesi J, Lewis MR, Dearnaley Det al., 2020, Metabolic profiles do not recover to normal after pelvic IMRT for high-risk prostate cancer., Publisher: ELSEVIER IRELAND LTD, Pages: S119-S119, ISSN: 0167-8140

Conference paper

Evans AM, O'Donovan C, Playdon M, Beecher C, Beger RD, Bowden JA, Broadhurst D, Clish CB, Dasari S, Dunn WB, Griffin JL, Hartung T, Hsu P-C, Huan T, Jans J, Jones CM, Kachman M, Kleensang A, Lewis MR, Monge ME, Mosley JD, Taylor E, Tayyari F, Theodoridis G, Torta F, Ubhi BK, Vuckovic Det al., 2020, Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC-MS based untargeted metabolomics practitioners, METABOLOMICS, Vol: 16, ISSN: 1573-3882

Journal article

Chen Q, Alexiadou K, Jones B, Sands C, Lewis MR, Bloom SR, Tan T, Li Jet al., 2020, Low-calorie intake: a key mechanism contributing to the metabolic impacts of Roux-en-Y gastric bypass surgery, 56th Annual Meeting of the European-Association-for-the-Study-of-Diabetes (EASD), Publisher: SPRINGER, Pages: S263-S264, ISSN: 0012-186X

Conference paper

Robinson O, Chadeau Hyam M, Karaman I, Climaco Pinto R, Ala-Korpela M, Handakas E, Fiorito G, Gao H, Heard A, Jarvelin M-R, Lewis M, Pazoki R, Polidoro S, Tzoulaki I, Wielscher M, Elliott P, Vineis Pet 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.

Journal article

GADGIL MD, SANDS C, LEWIS MR, KANAYA AM, KANDULA NR, HERRINGTON DMet al., 2020, 1581-P: Circulating Metabolites Are Associated with Glycemic Measures in South Asians, Diabetes, Vol: 69, Pages: 1581-P, ISSN: 0012-1797

Journal article

Takis P, Jimenez B, Sands C, Chekmeneva E, Lewis Met 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.

Journal article

Andreas NJ, Roy RB, Gomez-Romero M, Horneffer-van der Sluis V, Lewis MR, Camuzeaux SSM, Jimenez B, Posma JM, Tientcheu L, Egere U, Sillah A, Togun T, Holmes E, Kampmann Bet 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.

Journal article

Maciejewski M, Sands C, Nair N, Ling S, Verstappen S, Hyrich K, Rams M, Barton A, Ziemek D, Lewis M, Plant Det 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

Conference paper

Sands C, Wolfer A, DS Correia G, Sadawi N, Ahmed A, Jimenez B, Lewis M, Glen R, Nicholson J, Pearce Jet 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

Journal article

Wang X, Nijman R, Camuzeaux S, Sands C, Jackson H, Kaforou M, Emonts M, Herberg J, Maconochie I, Carrol E, Paulus S, Zenz W, Coin L, Flier MVD, Groot RD, Martinon-Torres F, Schlapbach LJ, Pollard A, Fink C, Kuijpers TT, Anderson S, Lewis M, Levin M, McClure M, EUCLIDS consortiumet 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.

Journal article

Whiley L, Chekmeneva E, Berry DJ, Jimenez B, Yuen AHY, Salam A, Hussain H, Witt M, Takats Z, Nicholson JK, Lewis MRet 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.

Journal article

Gafson AR, Savva C, Thorne T, David MJ, Gomez-Romero B, Lewis M, Nicholas R, Heslegrave A, Zetterberg H, Matthews Pet 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

Journal article

Whiley LW, Nye L, Grant I, Andreas N, Chappell K, Sarafian MHS, Misra R, Plumb R, Lewis M, Nicholson J, Holmes E, Swann J, Wilson Iet al., 2019, Ultrahigh-performance liquid chromatography tandem mass spectrometry with electrospray ionization quantification of tryptophan metabolites and markers of gut health in serum and plasmaapplication 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.

Journal article

Wolfer K, Lewis M, Sarafian M, Taylor DR, Vincent R, Patel VC, McPhail MJWet 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

Conference paper

Izzi-Engbeaya CN, Comninos AN, Clarke S, Abbara A, Lewis M, Holmes E, Nicholson J, Tan T, Rutter G, Dhillo Wet 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.

Journal article

Jimenez B, Holmes E, Heude C, Tolson RFM, Harvey N, Lodge SL, Chetwynd AJ, Cannet C, Fang F, Pearce JTM, Lewis MR, Viant MR, Lindon JC, Spraul M, Schaefer H, Nicholson JKet 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.

Journal article

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