Imperial College London

ProfessorTimothyEbbels

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Professor of Biomedical Data Science
 
 
 
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Contact

 

+44 (0)20 7594 3160t.ebbels Website

 
 
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Location

 

315DBurlington DanesHammersmith Campus

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Summary

 

Publications

Publication Type
Year
to

164 results found

Judge MT, Ebbels TMD, 2022, Problems, principles and progress in computational annotation of NMR metabolomics data., Metabolomics, Vol: 18

BACKGROUND: Compound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for 1H 1-dimensional (1H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards. AIM OF REVIEW: This review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions. KEY SCIENTIFIC CONCEPTS OF REVIEW: We begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.

Journal article

Ebbels T, Wieder C, Lai P-J, 2022, Single sample pathway analysis in metabolomics: performance evaluation and application, BMC Bioinformatics, Vol: 23, Pages: 1-26, ISSN: 1471-2105

BackgroundSingle sample pathway analysis (ssPA) transforms molecular level omics data to the pathway level, enabling the discovery of patient-specific pathway signatures. Compared to conventional pathway analysis, ssPA overcomes the limitations by enabling multi-group comparisons, alongside facilitating numerous downstream analyses such as pathway-based machine learning. While in transcriptomics ssPA is a widely used technique, there is little literature evaluating its suitability for metabolomics. Here we provide a benchmark of established ssPA methods (ssGSEA, GSVA, SVD (PLAGE), and z-score) alongside the evaluation of two novel methods we propose: ssClustPA and kPCA, using semi-synthetic metabolomics data. We then demonstrate how ssPA can facilitate pathway-based interpretation of metabolomics data by performing a case-study on inflammatory bowel disease mass spectrometry data, using clustering to determine subtype-specific pathway signatures.ResultsWhile GSEA-based and z-score methods outperformed the others in terms of recall, clustering/dimensionality reduction-based methods provided higher precision at moderate-to-high effect sizes. A case study applying ssPA to inflammatory bowel disease data demonstrates how these methods yield a much richer depth of interpretation than conventional approaches, for example by clustering pathway scores to visualise a pathway-based patient subtype-specific correlation network. We also developed the sspa python package (freely available at https://pypi.org/project/sspa/), providing implementations of all the methods benchmarked in this study.ConclusionThis work underscores the value ssPA methods can add to metabolomic studies and provides a useful reference for those wishing to apply ssPA methods to metabolomics data.

Journal article

Wieder C, Bundy JG, Frainay C, Poupin N, Rodriguez-Mier P, Vinson F, Cooke J, Lai RPJ, Jourdan F, Ebbels TMDet al., 2022, Avoiding the Misuse of Pathway Analysis Tools in Environmental Metabolomics, ENVIRONMENTAL SCIENCE & TECHNOLOGY, ISSN: 0013-936X

Journal article

Ostman JR, Pinto RC, Ebbels TMD, Thysell E, Hallmans G, Moazzami AAet al., 2022, Identification of prediagnostic metabolites associated with prostate cancer risk by untargeted mass spectrometry-based metabolomics: A case-control study nested in the Northern Sweden Health and Disease Study, INTERNATIONAL JOURNAL OF CANCER, Vol: 151, Pages: 2115-2127, ISSN: 0020-7136

Journal article

Chan Q, Wren G, Lau CH, Ebbels T, Gibson R, Loo RL, Aljuraiban G, Posma J, Dyer A, Steffen L, Rodriguez B, Appel L, Daviglus M, Elliott P, Stamler J, Holmes E, Van Horn Let al., 2022, Blood pressure interactions with the DASH dietary pattern, sodium, and potassium: The International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP), The American Journal of Clinical Nutrition, Vol: 116, Pages: 216-229, ISSN: 1938-3207

BackgroundAdherence to the Dietary Approaches to Stop Hypertension (DASH) diet enhances potassium intake and reduces sodium intake and blood pressure (BP), but the underlying metabolic pathways are unclear.ObjectiveAmong free-living populations, delineate metabolic signatures associated with the DASH diet adherence, 24-hr urinary sodium and potassium excretions and the potential metabolic pathways involved.Design24-hr urinary metabolic profiling by proton nuclear magnetic resonance spectroscopy was used to characterize the metabolic signatures associated with the DASH dietary pattern score (DASH score) and 24-hr excretion of sodium and potassium among participants in the United States (n=2,164) and United Kingdom (n= 496) enrolled in the International Study of Macro- and Micronutrients and Blood Pressure (INTERMAP). Multiple linear regression and cross-tabulation analyses were used to investigate the DASH-BP relation and its modulation by sodium and potassium. Potential pathways associated with DASH adherence, sodium and potassium excretion, and BP were identified using mediation analyses and metabolic reaction networks.ResultsAdherence to DASH diet was associated with urinary potassium excretion (correlation coefficient, r = 0.42, P<0.0001). In multivariable regression analyses, a five-point higher DASH score (range 7 to 35) was associated with a lower systolic BP by 1.35 mmHg (95% confidence interval: -1.95, -0.80, P=1.2 × 10−5); control of the model for potassium but not sodium attenuated the DASH-BP relation. Two common metabolites (hippurate and citrate) mediated the potassium-BP and DASH-BP relationships, while five metabolites (succinate, alanine, S-methyl cysteine sulfoxide, 4-hydroxyhippurate, phenylacetylglutamine) were found specific to the DASH-BP relation.ConclusionsGreater adherence to DASH diet is associated with lower BP and higher potassium intake across levels of sodium intake. The DASH diet recommends greater intake of fruits, veget

Journal article

Climaco Pinto R, Karaman I, Lewis MR, Hällqvist J, Kaluarachchi M, Graça G, Chekmeneva E, Durainayagam B, Ghanbari M, Ikram MA, Zetterberg H, Griffin J, Elliott P, Tzoulaki I, Dehghan A, Herrington D, Ebbels Tet al., 2022, Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets., Analytical Chemistry, Vol: 94, Pages: 5493-5503, ISSN: 0003-2700

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.

Journal article

Wieder C, Lai RPJ, Ebbels T, 2022, Single sample pathway analysis in metabolomics: performance evaluation and application, Publisher: Cold Spring Harbor Laboratory

<jats:title>Abstract</jats:title><jats:p>Single sample pathway analysis (ssPA) transforms molecular level omics data to the pathway level, enabling the discovery of patient-specific pathway signatures. Compared to conventional pathway analysis, ssPA overcomes the limitations by enabling multi-group comparisons, alongside facilitating numerous downstream analyses such as pathway-based machine learning. While in transcriptomics ssPA is a widely used technique, there is little literature evaluating its suitability for metabolomics. Here we provide a thorough benchmark of established ssPA methods (ssGSEA, GSVA, SVD (PLAGE), and z-score) using semi-synthetic metabolomics data, alongside the evaluation of two novel methods we propose: ssClustPA and kPCA. While GSEA-based and z-score methods outperformed the others in terms of recall, clustering/dimensionality reduction-based methods provided higher precision at moderate-to-high effect sizes. A case study applying ssPA to inflammatory bowel disease demonstrates how these methods yield a much richer depth of interpretation than conventional approaches, for example by clustering pathway scores to visualise a pathway-based patient subtype-specific correlation network. We also developed the sspa python package (freely available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://pypi.org/project/sspa/">https://pypi.org/project/sspa/</jats:ext-link>), providing implementations of all the methods benchmarked in this study. This work underscores the value ssPA methods can add to metabolomic studies and provides a useful reference for those wishing to apply ssPA methods to metabolomics data.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>Pathway analysis is a computational method used to draw insights from omics data by identifying groups of molecules (biological pathways) whi

Working paper

Graca G, Cai Y, Lau C-H, Vorkas PA, Lewis MR, Want EJ, Herrington D, Ebbels Tet al., 2022, Automated annotation of untargeted all-ion fragmentation LC-MS metabolomics data with MetaboAnnotatoR, Analytical Chemistry, Vol: 94, Pages: 3446-3455, ISSN: 0003-2700

Untargeted metabolomics and lipidomics LC-MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC-MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here we propose a novel approach for automated annotation of isotopologues, adducts and in-source fragments from AIF LC-MS datasets by combining correlation-based parent-fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82- 92% and 82-85% respectively, were obtained for features found in the highest-rank scores (1-5). These results equal or outperform those obtained using MS-DIAL software, the current state-of-the-art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60-89%) and recall (10-88%) particularly for datasets dominated by non-lipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).

Journal article

Wieder C, Frainay C, Poupin N, Rodriguez-Mier P, Vinson F, Cooke J, Lai RPJ, Bundy JG, Jourdan F, Ebbels Tet al., 2021, Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis, PLOS COMPUTATIONAL BIOLOGY, Vol: 17, ISSN: 1553-734X

Journal article

Harrill JA, Viant MR, Yauk CL, Sachana M, Gant TW, Auerbach SS, Beger RD, Bouhifd M, O'Brien J, Burgoon L, Caiment F, Carpi D, Chen T, Chorley BN, Colbourne J, Corvi R, Debrauwer L, O'Donovan C, Ebbels TMD, Ekman DR, Faulhammer F, Gribaldo L, Hilton GM, Jones SP, Kende A, Lawson TN, Leite SB, Leonards PEG, Luijten M, Martin A, Moussa L, Rudaz S, Schmitz O, Sobanski T, Strauss V, Vaccari M, Vijay V, Weber RJM, Williams AJ, Williams A, Thomas RS, Whelan Met al., 2021, Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology, REGULATORY TOXICOLOGY AND PHARMACOLOGY, Vol: 125, ISSN: 0273-2300

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

Heinecke A, Ye L, De Iorio M, Ebbels Tet al., 2021, Bayesian Deconvolution and Quantification of Metabolites from J-Resolved NMR Spectroscopy, BAYESIAN ANALYSIS, Vol: 16, Pages: 425-458, ISSN: 1931-6690

Journal article

Peluso A, Glen R, Ebbels T, 2021, Multiple-testing correction in metabolome-wide association studies, BMC Bioinformatics, Vol: 22, ISSN: 1471-2105

Background:The search for statistically significant relationships between molecular markers and outcomes is challenging when dealing with high-dimensional, noisy and collinear multivariate omics data, such as metabolomic profiles. Permutation procedures allow for the estimation of adjusted significance levels without assuming independence among metabolomic variables. Nevertheless, the complex non-normal structure of metabolic profiles and outcomes may bias the permutation results leading to overly conservative threshold estimates i.e. lower than those from a Bonferroni or Sidak correction.Methods:Within a univariate permutation procedure we employ parametric simulation methods based on the multivariate (log-)Normal distribution to obtain adjusted significance levels which are consistent across different outcomes while effectively controlling the type I error rate. Next, we derive an alternative closed-form expression for the estimation of the number of non-redundant metabolic variates based on the spectral decomposition of their correlation matrix. The performance of the method is tested for different model parametrizations and across a wide range of correlation levels of the variates using synthetic and real data sets.Results:Both the permutation-based formulation and the more practical closed form expression are found to give an effective indication of the number of independent metabolic effects exhibited by the system, while guaranteeing that the derived adjusted threshold is stable across outcome measures with diverse properties.

Journal article

Lau CH, Taylor-Bateman V, Vorkas PA, Gomes Da Graca G, Vu T-H, Hou L, Chekmeneva E, Ebbels T, Chan Q, Van Horn L, Holmes Eet al., 2020, Metabolic signatures of gestational weight gain and postpartum weight loss in a lifestyle intervention study of overweight and obese women, Metabolites, Vol: 10, ISSN: 2218-1989

BACKGROUND: Overweight and obesity amongst women of reproductive age are increasingly common in developed economies and are shown to adversely affect birth outcomes and both childhood and adulthood health risks in the offspring. Metabolic profiling in conditions of overweight and obesity in pregnancy could potentially be applied to elucidate the molecular basis of the adverse effects of gestational weight gain (GWG) and postpartum weight loss (WL) on future risks for cardiovascular disease (CVD) and other chronic diseases. METHODS: Biofluid samples were collected from 114 ethnically diverse pregnant women with body mass index (BMI) between 25 and 40 kg/m2 from Chicago (US), as part of a randomized lifestyle intervention trial (Maternal Offspring Metabolics: Family Intervention Trial; NCT01631747). At 15 weeks, 35 weeks of gestation, and at 1 year postpartum, the blood plasma lipidome and metabolic profile of urine samples were analyzed by liquid chromatography mass spectrometry (LC-MS) and 1H nuclear magnetic resonance spectroscopy (1H NMR) respectively. RESULTS: Urinary 4-deoxyerythronic acid and 4-deoxythreonic acid were found to be positively correlated to BMI. Seventeen plasma lipids were found to be associated with GWG and 16 lipids were found to be associated with WL, which included phosphatidylinositols (PI), phosphatidylcholines (PC), lysophospholipids (lyso-), sphingomyelins (SM) and ether phosphatidylcholine (PC-O). Three phospholipids found to be positively associated with GWG all contained palmitate side-chains, and amongst the 14 lipids that were negatively associated with GWG, seven were PC-O. Six of eight lipids found to be negatively associated with WL contained an 18:2 fatty acid side-chain. CONCLUSIONS: Maternal obesity was associated with characteristic urine and plasma metabolic phenotypes, and phospholipid profile was found to be associated with both GWG and postpartum WL in metabolically healthy pregnant women with overweight/obesity. Postpartu

Journal article

Ashrafian H, Sounderajah V, Glen R, Ebbels T, Blaise BJ, Kalra D, Kultima K, Spjuth O, Tenori L, Salek R, Kale N, Haug K, Schober D, Rocca-Serra P, O'Donovan C, Steinbeck C, Cano I, de Atauri P, Cascante Met al., 2020, Metabolomics - the stethoscope for the 21st century, Medical Principles and Practice, Vol: 30, Pages: 301-310, ISSN: 1011-7571

Metabolomics offers systematic identification and quantification of all metabolic products from the human body. This field could provide clinicians with new sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualised level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice and discuss the translational challenges that the field faces. We searched PubMed, Medline and Embase for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and NMR based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use centre around scalability of data interpretation, standardisation of sample handling practice and e-infrastructure. Routine utilisation of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.

Journal article

Iacovacci J, Peluso A, Ebbels T, Ralser M, Glen Ret al., 2020, Extraction and integration of genetic networks from short-profile omic data sets, Metabolites, Vol: 10, ISSN: 2218-1989

Mass spectrometry technologies are widely used in the fields of ionomics and metabolomics to simultaneously profile the intracellular concentrations of, e.g., amino acids or elements in genome-wide mutant libraries. These molecular or sub-molecular features are generally non-Gaussian and their covariance reveals patterns of correlations that reflect the system nature of the cell biochemistry and biology. Here, we introduce two similarity measures, the Mahalanobis cosine and the hybrid Mahalanobis cosine, that enforce information from the empirical covariance matrix of omics data from high-throughput screening and that can be used to quantify similarities between the profiled features of different mutants. We evaluate the performance of these similarity measures in the task of inferring and integrating genetic networks from short-profile ionomics/metabolomics data through an analysis of experimental data sets related to the ionome and the metabolome of the model organism S. cerevisiae. The study of the resulting ionome–metabolome Saccharomyces cerevisiae multilayer genetic network, which encodes multiple omic-specific levels of correlations between genes, shows that the proposed measures can provide an alternative description of relations between biological processes when compared to the commonly used Pearson’s correlation coefficient and have the potential to guide the construction of novel hypotheses on the function of uncharacterised genes

Journal article

Wu C-T, Wang Y, Wang Y, Ebbels T, Karaman I, Graca G, Pinto R, Herrington DM, Wang Y, Yu Get al., 2020, Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection, BIOINFORMATICS, Vol: 36, Pages: 2862-2871, ISSN: 1367-4803

Journal article

Griep LMO, Chekmeneva E, Stamler J, Van Horn L, Chan Q, Daviglus M, Frost GS, Ebbels TM, Holmes E, Elliott Pet al., 2020, A Metabolome-wide Association Study of Plant Food Consumption With Blood Pressure, Scientific Sessions of the American-Heart-Association on Epidemiology and Prevention/Lifestyle and Cardiometabolic Health, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322

Conference paper

Jendoubi Bedhiafi T, Ebbels T, 2020, Integrative analysis of time course metabolic data and biomarker discovery, BMC Bioinformatics, Vol: 21, ISSN: 1471-2105

BackgroundMetabolomics time-course experiments provide the opportunity to understand the changes to an organism by observing the evolution of metabolic profiles in response to internal or external stimuli. Along with other omic longitudinal profiling technologies, these techniques have great potential to uncover complex relations between variations across diverse omic variables and provide unique insights into the underlying biology of the system. However, many statistical methods currently used to analyse short time-series omic data are i) prone to overfitting, ii) do not fully take into account the experimental design or iii) do not make full use of the multivariate information intrinsic to the data or iv) are unable to uncover multiple associations between different omic data. The model we propose is an attempt to i) overcome overfitting by using a weakly informative Bayesian model, ii) capture experimental design conditions through a mixed-effects model, iii) model interdependencies between variables by augmenting the mixed-effects model with a conditional auto-regressive (CAR) component and iv) identify potential associations between heterogeneous omic variables by using a horseshoe prior.ResultsWe assess the performance of our model on synthetic and real datasets and show that it can outperform comparable models for metabolomic longitudinal data analysis. In addition, our proposed method provides the analyst with new insights on the data as it is able to identify metabolic biomarkers related to treatment, infer perturbed pathways as a result of treatment and find significant associations with additional omic variables. We also show through simulation that our model is fairly robust against inaccuracies in metabolite assignments. On real data, we demonstrate that the number of profiled metabolites slightly affects the predictive ability of the model.ConclusionsOur single model approach to longitudinal analysis of metabolomics data provides an approach simultane

Journal article

Gibson R, Lau C, Loo RL, Ebbels T, Chekmeneva E, Dyer A, Miura K, Ueshima H, Zhao L, Daviglus M, Stamler J, Van Horn L, Elliott P, Holmes E, Chan Qet al., 2019, The association of fish consumption and its urinary metabolites with cardiovascular risk factors: The International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP), American Journal of Clinical Nutrition, Vol: 111, Pages: 280-290, ISSN: 0002-9165

BackgroundResults from observational studies regarding associations between fish (including shellfish) intake and cardiovascular disease risk factors, including blood pressure (BP) and BMI, are inconsistent.ObjectiveTo investigate associations of fish consumption and associated urinary metabolites with BP and BMI in free-living populations.MethodsWe used cross-sectional data from the International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP), including 4680 men and women (40–59 y) from Japan, China, the United Kingdom, and United States. Dietary intakes were assessed by four 24-h dietary recalls and BP from 8 measurements. Urinary metabolites (2 timed 24-h urinary samples) associated with fish intake acquired from NMR spectroscopy were identified. Linear models were used to estimate BP and BMI differences across categories of intake and per 2 SD higher intake of fish and its biomarkers.ResultsNo significant associations were observed between fish intake and BP. There was a direct association with fish intake and BMI in the Japanese population sample (P trend = 0.03; fully adjusted model). In Japan, trimethylamine-N-oxide (TMAO) and taurine, respectively, demonstrated area under the receiver operating characteristic curve (AUC) values of 0.81 and 0.78 in discriminating high against low fish intake, whereas homarine (a metabolite found in shellfish muscle) demonstrated an AUC of 0.80 for high/nonshellfish intake. Direct associations were observed between urinary TMAO and BMI for all regions except Japan (P < 0.0001) and in Western populations between TMAO and BP (diastolic blood pressure: mean difference 1.28; 95% CI: 0.55, 2.02 mmHg; P = 0.0006, systolic blood pressure: mean difference 1.67; 95% CI: 0.60, 2.73 mmHg; P = 0.002).ConclusionsUrinary TMAO showed a stronger association with fish intake in the Japanese compared with the Western population sample. Urinary TMAO was directly associated with BP in the Western but not the Japanese popula

Journal article

Segura-Lepe M, Keun H, Ebbels T, 2019, Predictive modelling using pathway scores: robustness and significance of pathway collections, BMC Bioinformatics, Vol: 20, ISSN: 1471-2105

BackgroundTranscriptomic data is often used to build statistical models which are predictive of a given phenotype, such as disease status. Genes work together in pathways and it is widely thought that pathway representations will be more robust to noise in the gene expression levels. We aimed to test this hypothesis by constructing models based on either genes alone, or based on sample specific scores for each pathway, thus transforming the data to a ‘pathway space’. We progressively degraded the raw data by addition of noise and examined the ability of the models to maintain predictivity.ResultsModels in the pathway space indeed had higher predictive robustness than models in the gene space. This result was independent of the workflow, parameters, classifier and data set used. Surprisingly, randomised pathway mappings produced models of similar accuracy and robustness to true mappings, suggesting that the success of pathway space models is not conferred by the specific definitions of the pathway. Instead, predictive models built on the true pathway mappings led to prediction rules with fewer influential pathways than those built on randomised pathways. The extent of this effect was used to differentiate pathway collections coming from a variety of widely used pathway databases.ConclusionsPrediction models based on pathway scores are more robust to degradation of gene expression information than the equivalent models based on ungrouped genes. While models based on true pathway scores are not more robust or accurate than those based on randomised pathways, true pathways produced simpler prediction rules, emphasizing a smaller number of pathways.

Journal article

Tzoulaki I, Castagné R, Boulangé CL, Karaman I, Chekmeneva E, Evangelou E, Ebbels TMD, Kaluarachchi MR, Chadeau-Hyam M, Mosen D, Dehghan A, Moayyeri A, Ferreira DLS, Guo X, Rotter JI, Taylor KD, Kavousi M, De Vries PS, Lehne B, Loh M, Hofman A, Nicholson JK, Chambers J, Gieger C, Holmes E, Tracy R, Kooner J, Greenland P, Franco OH, Herrington D, Lindon JC, Elliott Pet al., 2019, Serum metabolic signatures of coronary and carotid atherosclerosis and subsequent cardiovascular disease, European Heart Journal, Vol: 40, Pages: 2883-2896, ISSN: 1522-9645

Aims: To characterise serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD). Methods and Results: We used untargeted one-dimensional (1D) serum metabolic profiling by proton (1H) nuclear magnetic resonance (NMR) spectroscopy among 3,867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3,569 participants from the Rotterdam and LOLIPOP Studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 NMR measured metabolites were associated with CAC and/or IMT, P =1.3x10-14 to 6.5x10-6 (discovery), P =4.2x10-14 to 4.4x10-2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched-chain and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide and lactate as well as apolipoprotein B (P <0.05). Conclusion: Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclero

Journal article

Afonso C, Barrow MP, Davies AN, Delsuc M-A, Ebbels T, Fernandez-Lima F, Gauchotte-Lindsay C, Giusti P, Goodacre R, Hertkorn N, Jansen JJ, Jones D, Kew W, Kuhn S, Le Guennec A, Lubben A, Parkinson J, Paša-Tolić L, Rogers S, Rudd TR, Schoenmakers PJ, Sidebottom P, Summerfield S, Tinnevelt GH, Trifirò G, Trygg J, van der Hooft JJJet al., 2019, Data mining and visualisation: general discussion., Faraday Discuss, Vol: 218, Pages: 354-371

Journal article

Ebbels TMD, De Iorio M, Stephens DA, 2019, Statistical methods in metabolomics, Handbook of Statistical Genomics, Pages: 949-975, ISBN: 9781119429142

Metabolomics studies the levels of small molecules in living systems and can reveal much about an organism's state of health or disease. The data is complex and requires tailored data processing and modelling approaches. Here we review recent developments in the field, including data preprocessing, univariate and multivariate approaches, network and pathway analysis, and methods aiding metabolite identification.

Book chapter

Viant MR, Ebbels TMD, Beger RD, Ekman DR, Epps DJT, Kamp H, Leonards PEG, Loizou GD, MacRae JI, van Ravenzwaay B, Rocca-Serra P, Salek RM, Walk T, Weber RJMet al., 2019, Use cases, best practice and reporting standards for metabolomics in regulatory toxicology, Nature Communications, Vol: 10, ISSN: 2041-1723

Metabolomics is a widely used technology in academic research, yet its application to regulatory science has been limited. The most commonly cited barrier to its translation is lack of performance and reporting standards. The MEtabolomics standaRds Initiative in Toxicology (MERIT) project brings together international experts from multiple sectors to address this need. Here, we identify the most relevant applications for metabolomics in regulatory toxicology and develop best practice guidelines, performance and reporting standards for acquiring and analysing untargeted metabolomics and targeted metabolite data. We recommend that these guidelines are evaluated and implemented for several regulatory use cases.

Journal article

Gao Q, Dragsted LO, Ebbels T, 2019, Comparison of Bi- and Tri-Linear PLS models for variable selection in metabolomic time-series experiments, Metabolites, Vol: 9, ISSN: 2218-1989

Metabolomic studies with a time-series design are widely used for discovery and validation of biomarkers. In such studies, changes of metabolic profiles over time under different conditions (e.g., control and intervention) are compared, and metabolites responding differently between the conditions are identified as putative biomarkers. To incorporate time-series information into the variable (biomarker) selection in partial least squares regression (PLS) models, we created PLS models with different combinations of bilinear/trilinear X and group/time response dummy Y. In total, five PLS models were evaluated on two real datasets, and also on simulated datasets with varying characteristics (number of subjects, number of variables, inter-individual variability, intra-individual variability and number of time points). Variables showing specific temporal patterns observed visually and determined statistically were labelled as discriminating variables. Bootstrapped-VIP scores were calculated for variable selection and the variable selection performance of five PLS models were assessed based on their capacity to correctly select the discriminating variables. The results showed that the bilinear PLS model with group × time response as dummy Y provided the highest recall (true positive rate) of 83–95% with high precision, independent of most characteristics of the datasets. Trilinear PLS models tend to select a small number of variables with high precision but relatively high false negative rate (lower power). They are also less affected by the noise compared to bilinear PLS models. In datasets with high inter-individual variability, bilinear PLS models tend to provide higher recall while trilinear models tend to provide higher precision. Overall, we recommend bilinear PLS with group x time response Y for variable selection applications in metabolomics intervention time series studies.

Journal article

Peters K, Bradbury J, Bergmann S, Capuccini M, Cascante M, de Atauri P, Ebbels TMD, Foguet C, Glen R, Gonzalez-Beltran A, Günther UL, Handakas E, Hankemeier T, Haug K, Herman S, Holub P, Izzo M, Jacob D, Johnson D, Jourdan F, Kale N, Karaman I, Khalili B, Khonsari PE, Kultima K, Lampa S, Larsson A, Ludwig C, Moreno P, Neumann S, Novella JA, O'Donovan C, Pearce JTM, Peluso A, Piras ME, Pireddu L, Reed MAC, Rocca-Serra P, Roger P, Rosato A, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone S-A, Selivanov V, Spjuth O, Schober D, Thévenot EA, Tomasoni M, van Rijswijk M, van Vliet M, Viant MR, Weber RJM, Zanetti G, Steinbeck Cet al., 2019, PhenoMeNal: Processing and analysis of metabolomics data in the cloud, GigaScience, Vol: 8, ISSN: 2047-217X

Background: Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent-and sometimes incompatible-analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings: PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm. Conclusions: PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible and shareable metabolomics data analysis platforms which are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adap

Journal article

Ebbels TMD, Pearce JTM, Sadawi N, Gao J, Glen RCet al., 2019, Big Data and Databases for Metabolic Phenotyping, HANDBOOK OF METABOLIC PHENOTYPING, Editors: Lindon, Nicholson, Holmes, Publisher: ELSEVIER SCIENCE BV, Pages: 329-367, ISBN: 978-0-12-812293-8

Book chapter

Ebbels TMD, Karaman I, Graca G, 2019, Processing and Analysis of Untargeted Multicohort NMR Data, NMR-BASED METABOLOMICS: METHODS AND PROTOCOLS, Editors: Gowda, Raftery, Publisher: HUMANA PRESS INC, Pages: 453-470, ISBN: 978-1-4939-9689-6

Book chapter

Gibson R, Lau C-HE, Chan Q, Chekmeneva E, Loo R, Ebbels TM, Dyer AR, Miura K, Ueshima H, Zhao L, Daviglus ML, Elliott P, Stamler J, Holmes E, Van Horn Let al., 2019, Cross-Sectional Investigation of the Relationship Between Fish Consumption and Its Urinary Biomarkers With Blood Pressure Across Asian and Western Populations: Results From 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

Conference paper

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