Imperial College London

Emeritus ProfessorJeremyNicholson

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Emeritus Professor of Biological Chemistry
 
 
 
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Contact

 

+44 (0)20 7594 3195j.nicholson Website

 
 
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Assistant

 

Ms Wendy Torto +44 (0)20 7594 3225

 
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Location

 

Office no. 665Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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986 results found

Kadar H, Dubus J, Dutot J, Hedjazi L, Srinivasa S, Fitch KV, Grinspoon SK, Nicholson JK, Dumas M-E, Gauguier Det al., 2016, A multiplexed targeted assay for high-throughput quantitative analysis of serum methylamines by ultra performance liquid chromatography coupled to high resolution mass spectrometry, Archives of Biochemistry and Biophysics, Vol: 597, Pages: 12-20, ISSN: 1096-0384

Methylamines are biologically-active metabolites present in serum and urine samples, which play complex roles in metabolic diseases. Methylamines can be detected by proton nuclear magnetic resonance (NMR), but specific methods remain to be developed for their routine assay in human serum in clinical settings. Here we developed and validated a novel reliable “methylamine panel” method for simultaneous quantitative analysis of trimethylamine (TMA), its major detoxification metabolite trimethylamine-N-oxide (TMAO), and precursors choline, betaine and l-carnitine in human serum using Ultra Performance Liquid Chromatography (UPLC) coupled to High Resolution Mass Spectrometry (HRMS). Metabolite separation was carried out on a HILIC stationary phase. For all metabolites, the assay was linear in the range of 0.25–12.5 μmol/L and enabled to reach limit of detection of about 0.10 μmol/L. Relative standard deviations were below 16% for the three levels of concentrations. We demonstrated the strong reliability and robustness of the method, which was applied to serum samples from healthy individuals to establish the range of concentrations of the metabolites and their correlation relationships and detect gender differences. Our data provide original information for implementing in a clinical environment a MS-based diagnostic method with potential for targeted metabolic screening of patients at risk of cardiometabolic diseases.

Journal article

Everett JR, Lindon JC, Nicholson JK, 2016, Pharmacometabonomics and predictive metabonomics: New tools for personalized medicine, Metabolic Phenotyping in Personalized and Public Healthcare, Pages: 137-165, ISBN: 9780128003442

© 2016 Elsevier Inc. All rights reserved. Metabonomics is an established technology concerned with the analysis of human and animal biofluids to reveal and understand changes in metabolite profiles resulting from interventions such as drug administration, disease, aging, and genetic change. Typically, these studies are performed through analysis of metabolites in biofluids such as urine or blood plasma by using powerful analytical technologies such as nuclear magnetic resonance spectroscopy or liquid chromatography-mass spectrometry. Pharmacometabonomics is a newer technology concerned with the use of predose metabolite profiles to predict responses to drugs prior to dosing. Differences observed in responses to drugs such as varying pharmacokinetics, metabolism, efficacy, or toxicity in different subgroups of patients may be correlated with differences in predose metabolite profiles between those particular patient subgroups. This, then, enables prediction of drug response in patients on the basis of predose metabolite profile analysis. Pharmacometabonomics is, thus, a valuable aid to decision making in stratified or Personalized Medicine.

Book chapter

Holmes E, Nicholson JK, Li J, Darzi AWet al., 2016, Phenotyping the patient journey, Metabolic Phenotyping in Personalized and Public Healthcare, Pages: 49-74, ISBN: 9780128003442

© 2016 Elsevier Inc. All rights reserved. This chapter describes the concept of the patient journey, that is, the various interactions between a patient and a medical team as the patient first encounters the system, is diagnosed, treated, and followed up after whatever course of action was deemed appropriate. The various bottlenecks in the process are explained. As a new paradigm, the role of metabolic phenotyping (metabotyping) in monitoring the patient journey is discussed and examples are provided. The potential of such metabolic phenotyping in the clinic has implications in terms of stratified or personalized medicine, including adding information to aid diagnosis or to allow better prognosis, and these implications are listed. Finally, one example of the process, a dedicated phenome center, is illustrated.

Book chapter

Holmes E, Nicholson JK, Darzi AW, Lindon JCet al., 2016, Preface, ISBN: 9780128003442

Book

Lindon JC, Nicholson JK, Holmes E, Darzi AWet al., 2016, Future visions for clinical metabolic phenotyping: Prospects and challenges, Metabolic Phenotyping in Personalized and Public Healthcare, Pages: 369-388, ISBN: 9780128003442

© 2016 Elsevier Inc. All rights reserved. In this final chapter, the current state of the art in metabolic phenotyping is summarized. The challenges for future integration of the topic into mainstream clinical and health studies are delineated and discussed. These include the need for validation, standard protocols, and the problems associated with "big data." The prospects for metabolic phenotyping in clinical and epidemiologic studies are described. Potential new phenotyping outputs and possible impacts of metabolic phenotyping on medicine are listed.

Book chapter

Kenderdine S, Nicholson JK, Mason I, 2016, Modeling people and populations: Exploring medical visualization through immersive interactive virtual environments, Metabolic Phenotyping in Personalized and Public Healthcare, Pages: 333-367, ISBN: 9780128003442

© 2016 Elsevier Inc. All rights reserved. This chapter explores developments in immersive interactive virtual environments for solving core challenges represented by advances in medical imaging and the emergence of medical big data. Visualization systems for large-scale data sets are increasingly focused on effectively representing many levels of complexity. The discussion is foregrounded by an overview of advances in medical imaging and selected existing visualization paradigms, together with a description of essential data management requirements. In addition to describing a range of distinct state-of-the-art visualization environments at key research universities, a corresponding set of case studies are introduced to explore the affordances and constraints of these systems in relation to the experimental research undertaken with them. The discussion includes new developments in, and requirements for, this emerging field of research.

Book chapter

Ashrafian H, Athanasiou T, Nicholson JK, Darzi AWet al., 2016, Unmet Medical Needs, Metabolic Phenotyping in Personalized and Public Healthcare, Pages: 1-15, ISBN: 9780128003442

© 2016 Elsevier Inc. All rights reserved. Despite dramatic strides in health care, there are persistent and far-reaching medical needs that remain unmet. These include challenges in disease management, health care technology, socioeconomics, and health processes. These should be considered in a context of shifting pathology, where global trends in communicable and noncommunicable diseases remain in flux. Personalized Medicine can potentially address many of these persistent needs. We define this as the tailored management and/or prevention of disease according to the specific characteristics of a stratified individual, subpopulation, or population to enhance patient care. These characteristics are derived from the integrated evaluation of phenotype, genotype, and treatment bioresponses realized through a systems biomedicine "-omics" approach. This employs complex multivariate, network, and hierarchical computation in the context of best evidence-based practice. It offers precision in diagnosis and treatments, in addition to the generation of targeted therapeutics. This approach may offer novel strategies in addressing future unmet medical needs.

Book chapter

Holmes E, Nicholson JK, Darzi AW, Lindon JCet al., 2016, Metabolic Phenotyping in Personalized and Public Healthcare, ISBN: 9780128003442

© 2016 Elsevier Inc. All rights reserved. Metabolic Phenotyping in Personalized and Public Healthcare provides information on the widespread recognition that a personalized or stratified approach to patient treatment may offer a more efficient and effective healthcare solution than phenotype-led approaches. In order to achieve that objective, a deep personal description is required at the level of the genome, proteome, metabolome, or preferably a combination of these aided by technology. This book, edited and written by the outstanding luminaries of this evolving field, evaluates metabolic profiling and its uses across personalized and population healthcare, while also covering the advent of new technology fields, such as surgical metabonomics. In addition, the text presents specific examples of where this technology has been used clinically and with efficacy, pointing towards a framework and protocol for usage as it hits the clinical mainstream.

Book

Li JV, nicholson J, holmes E, darzi Aet al., 2016, Chapter 3 - Phenotyping the Patient Journey, Metabolic Phenotyping in Personalized and Public Healthcare, Publisher: Academic Press, ISBN: 9780128004142

HEALTHCARE. EDTED BY Elaine Holmes, Head of Computational and Systems Medicine Professor of Chemical ... Metabolic Phenotyping in Personalized and Public Healthcare authoritatively evaluates metabolic profiling and its uses ...

Book chapter

McPhail MJW, Shawcross D, Lewis MR, Coltart I, Want E, Veselkov K, Abeles RD, Kyriakides M, Pop O, Triantafyllou E, Antoniades CG, Quaglia A, Bernal W, Auzinger G, Coen M, Nicholson J, Wendon JA, Holmes E, Taylor-Robinson SD, Jassem W, O'Grady J, Heaton Net 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.

Journal article

Athersuch T, 2016, Metabolome analyses in exposome studies: Profiling methods for a vast chemical space, Archives of Biochemistry and Biophysics, Vol: 589, Pages: 177-186, ISSN: 1096-0384

Metabolic profiling (metabonomics/metabolomics) is now used routinely as a tool to provide information-rich datasets for biomarker discovery, prompting and augmenting detailed mechanistic studies. The experimental design and focus of any individual study will be reflected in the types of biomarkers that can be detected; toxicological studies will likely focus on markers of response to insult, whereas clinical case-control studies may yield diagnostic markers of disease. Population studies can make use of omics analyses, including metabonomics, to provide mechanistically-relevant markers that link environmental exposures to chronic disease endpoints. In this article, examples of how metabolic profiling has played a key role in molecular epidemiological analyses of chronic disease are presented, and how these reflect different aspects of the causal pathway. A commentary on the nature of metabolome analysis as a complex mixture problem as opposed to a coded, sequence or template problem is provided, alongside an overview of current and future analytical platforms that are being applied to meet this analytical challenge. Epidemiological studies are an important nexus for integrating various measures of the human exposome, and the ubiquity, diversity and functions of small molecule metabolites, represent an important way to link individual exposures, genetics and phenotype.

Journal article

Wolfer AM, Gaudin M, Taylor-Robinson SD, Holmes E, Nicholson JKet al., 2015, Development and Validation of a High-Throughput Ultrahigh-Performance Liquid Chromatography-Mass Spectrometry Approach for Screening of Oxylipins and Their Precursors., Analytical Chemistry, ISSN: 1086-4377

Lipid mediators, highly bioactive compounds synthesized from polyunsaturated fatty acids (PUFAs), have a fundamental role in the initiation and signaling of the inflammatory response. Although extensively studied in isolation, only a limited number of analytical methods offer a comprehensive coverage of the oxylipin synthetic cascade applicable to a wide range of human biofluids. We report the development of an ultrahigh-performance liquid chromatography-electrospray ionization triple quadrupole mass spectrometry (UHPLC-MS) assay to quantify oxylipins and their PUFA precursors in 100 μL of human serum, plasma, urine, and cell culture supernatant. A single 15 min UHPLC run enables the quantification of 43 oxylipins and 5 PUFAs, covering pro and anti-inflammatory lipid mediators synthesized across the cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP450) pathways. The method was validated in multiple biofluid matrixes (serum, plasma, urine, and cell supernatant) and suppliers, ensuring its suitability for large scale metabonomic studies. The approach is accurate, precise, and reproducible (RSD < 15%) over multiple days and concentrations. Very high sensitivity is achieved with limits of quantification inferior to picograms for the majority of analytes (0.05-125 pg) and linear range spanning up to 5 orders of magnitude. This enabled the quantification of the great majority of these analytes at their low endogenous level in human biofluids. We successfully applied the procedure to individuals undergoing a fasting intervention; oxylipin profiles highlighted significantly altered PUFA and inflammatory profiles in accordance with previously published studies as well as offered new insight on the modulation of the biosynthetic cascade responsible for the regulation of oxylipins.

Journal article

Mitra A, MacIntyre D, lee YS, Smith A, Marchesi J, Lehne B, Bhatia R, lyons D, Paraskevaidis E, Li J, holmes E, nicholson JK, bennett PR, kyrgiou Met al., 2015, Cervical intraepithelial neoplasia disease progression is associated with increased vaginal microbiome diversity, Scientific Reports, Vol: 5, ISSN: 2045-2322

Persistent infection with oncogenic Human Papillomavirus (HPV) is necessary for cervical carcinogenesis. Although evidence suggests that the vaginal microbiome plays a functional role in the persistence or regression of HPV infections, this has yet to be described in women with cervical intra-epithelial neoplasia (CIN). We hypothesised that increasing microbiome diversity is associated with increasing CIN severity. llumina MiSeq sequencing of 16S rRNA gene amplicons was used to characterise the vaginal microbiota of women with low-grade squamous intra-epithelial lesions (LSIL; n = 52), high-grade (HSIL; n = 92), invasive cervical cancer (ICC; n = 5) and healthy controls (n = 20). Hierarchical clustering analysis revealed an increased prevalence of microbiomes characterised by high-diversity and low levels of Lactobacillus spp. (community state type-CST IV) with increasing disease severity, irrespective of HPV status (Normal = 2/20,10%; LSIL = 11/52,21%; HSIL = 25/92,27%; ICC = 2/5,40%). Increasing disease severity was associated with decreasing relative abundance of Lactobacillus spp. The vaginal microbiome in HSIL was characterised by higher levels of Sneathia sanguinegens (P < 0.01), Anaerococcus tetradius (P < 0.05) and Peptostreptococcus anaerobius (P < 0.05) and lower levels of Lactobacillus jensenii (P < 0.01) compared to LSIL. Our results suggest advancing CIN disease severity is associated with increasing vaginal microbiota diversity and may be involved in regulating viral persistence and disease progression.

Journal article

Veselkov KA, McKenzie JS, Nicholson JK, 2015, Multivariate Data Analysis Methods for NMR-based Metabolic Phenotyping in Pharmaceutical and Clinical Research, NMR in Pharmaceutical Science, Editors: Everett, Harris, Lindon, Wilson, Publisher: John Wiley & Sons, Pages: 323-334, ISBN: 9781118660256

High-resolution NMR spectroscopy is applied for molecular phenotyping across a range of pharmaceutical and clinical applications such as drug toxicity, disease diagnostics, and personalized healthcare studies. A typical NMR profile of a biological sample contains tens of thousands of signals arising from hundreds of endogenous and exogenous metabolites. The generated data requires advanced computational workflows to translate raw spectroscopic data into pharmacology and clinically useful information. This article outlines various chemoinformatics strategies that maximize disease and pharmacologically relevant molecular information recovery from one-dimensional NMR spectra of biological samples. In broad terms, the outlined strategies involve (i) raw analytical signal preprocessing for improved information recovery, (ii) multivariate statistical explorative and predictive analyses of NMR biological spectra, and (iii) time-course analyses to address a range of pharmaceutically and clinically relevant questions.

Book chapter

Sarafian MH, Lewis MR, Pechlivanis A, Ralphs S, McPhail MJW, Patel VC, Dumas M-E, Holmes E, Nicholson Jet al., 2015, Bile Acid Profiling and Quantification in Biofluids Using Ultra-Performance Liquid Chromatography Tandem Mass Spectrometry, Analytical Chemistry, Vol: 87, Pages: 9662-9670, ISSN: 1520-6882

Bile acids are important end products of cholesterol metabolism. While they have been identified as key factors in lipid emulsification and absorption due to their detergent properties, bile acids have also been shown to act as signaling molecules and intermediates between the host and the gut microbiota. To further the investigation of bile acid functions in humans, an advanced platform for high throughput analysis is essential. Herein, we describe the development and application of a 15 min UPLC procedure for the separation of bile acid species from human biofluid samples requiring minimal sample preparation. High resolution time-of-flight mass spectrometry was applied for profiling applications, elucidating rich bile acid profiles in both normal and disease state plasma. In parallel, a second mode of detection was developed utilizing tandem mass spectrometry for sensitive and quantitative targeted analysis of 145 bile acid (BA) species including primary, secondary, and tertiary bile acids. The latter system was validated by testing the linearity (lower limit of quantification, LLOQ, 0.25–10 nM and upper limit of quantification, ULOQ, 2.5–5 μM), precision (≈6.5%), and accuracy (81.2–118.9%) on inter- and intraday analysis achieving good recovery of bile acids (serum/plasma 88% and urine 93%). The ultra performance liquid chromatography–mass spectrometry (UPLC-MS)/MS targeted method was successfully applied to plasma, serum, and urine samples in order to compare the bile acid pool compositional difference between preprandial and postprandial states, demonstrating the utility of such analysis on human biofluids.

Journal article

Heinzmann SS, Holmes E, Kochhar S, Nicholson JK, Schmitt-Kopplin Pet al., 2015, 2-Furoylglycine as a Candidate Biomarker of Coffee Consumption, JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, Vol: 63, Pages: 8615-8621, ISSN: 0021-8561

Journal article

Braun H, Kirchmair J, Williamson MJ, Makarov VA, Riabova OB, Glen RC, Sauerbrei A, Schmidtke Met al., 2015, Molecular mechanism of a specific capsid binder resistance caused by mutations outside the binding pocket, Antiviral Research, Vol: 123, Pages: 138-145, ISSN: 1872-9096

Enteroviruses cause various acute and chronic diseases. The most promising therapeutics for these infections are capsid-binding molecules. These can act against a broad spectrum of enteroviruses, but emerging resistant virus variants threaten their efficacy. All known enterovirus variants with high-level resistance toward capsid-binding molecules have mutations of residues directly involved in the formation of the hydrophobic binding site. This is a first report of substitutions outside the binding pocket causing this type of drug resistance: I1207K and I1207R of the viral capsid protein 1 of coxsackievirus B3. Both substitutions completely abolish the antiviral activity of pleconaril (a capsid-binding molecule) but do not affect viral replication rates in vitro. Molecular dynamics simulations indicate that the resistance mechanism is mediated by a conformational rearrangement of R1095, which is a neighboring residue of 1207 located at the heel of the binding pocket. These in

Journal article

Murrell DS, Cortes-Ciriano I, van Westen GJP, Stott IP, Bender A, Malliavin TE, Glen RCet al., 2015, Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules, Journal of Cheminformatics, Vol: 7, ISSN: 1758-2946

BackgroundIn silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process.Resultscamb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2).ConclusionsOverall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.

Journal article

Balog J, Kumar, Alexander J, Golf O, Huang J, Abbassi-Ghadi, Wiggins T, Abbassi-Ghadi N, Enyedi A, Kacska S, Kinross J, Hanna G, Nicholson JK, Takats Zet al., 2015, In vivo endoscopic tissue identification tool utilising Rapid Evaporative Ionization Mass Spectrometry (REIMS), Angewandte Chemie International Edition, Vol: 54, Pages: 11059-11062, ISSN: 1433-7851

Gastrointestinal cancers are a leading cause of mortality, accounting for 23 % of cancer-related deaths worldwide. In order to improve outcomes from these cancers, novel tissue characterization methods are needed to facilitate accurate diagnosis. Rapid evaporative ionization mass spectrometry (REIMS) is a technique developed for the in vivo classification of human tissue through mass spectrometric analysis of aerosols released during electrosurgical dissection. This ionization technique was further developed by utilizing surface induced dissociation and was integrated with an endoscopic polypectomy snare to allow in vivo analysis of the gastrointestinal tract. We tested the classification performance of this novel endoscopic REIMS method in vivo. It was shown to be capable of differentiating between healthy layers of the intestinal wall, cancer, and adenomatous polyps based on the REIMS fingerprint of each tissue type in vivo.

Journal article

Shoaie S, Ghaffari P, Kovatcheva-Datchary P, Mardinoglu A, Sen P, Pujos-Guillot E, de Wouters T, Juste C, Rizkalla S, Chilloux J, Hoyles L, Nicholson JK, MICRO-Obes consortium, Dore J, Dumas ME, Clement K, Bäckhed F, Nielsen Jet al., 2015, Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome, Cell Metabolism, Vol: 22, Pages: 320-331, ISSN: 1932-7420

The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.

Journal article

Holmes E, Wijeyesekera A, Taylor-Robinson SD, Nicholson JKet al., 2015, The promise of metabolic phenotyping in gastroenterology and hepatology, Nature Reviews Gastroenterology & Hepatology, Vol: 12, Pages: 458-471, ISSN: 1759-5053

Disease risk and treatment response are determined, at the individual level, by a complex historyof genetic and environmental interactions, including those with our endogenous microbiomes. Personalizedhealth care requires a deep understanding of patient biology that can now be measured using a range of‘‑omics’ technologies. Patient stratification involves the identification of genetic and/or phenotypic diseasesubclasses that require different therapeutic strategies. Stratified medicine approaches to disease diagnosis,prognosis and therapeutic response monitoring herald a new dimension in patient care. Here, we explore thepotential value of metabolic profiling as applied to unmet clinical needs in gastroenterology and hepatology.We describe potential applications in a number of diseases, with emphasis on large-scale population studiesas well as metabolic profiling on the individual level, using spectrometric and imaging technologies that willleverage the discovery of mechanistic information and deliver novel health care solutions to improve clinicalpathway management.

Journal article

Stahl SH, Yates JW, Nicholls AW, Kenna JG, Coen M, Ortega F, Nicholson JK, Wilson IDet al., 2015, Systems toxicology: modelling biomarkers of glutathione homeostasis and paracetamol metabolism, Drug Discovery Today: Technologies, Vol: 15, Pages: 9-14, ISSN: 1740-6749

One aim of systems toxicology is to deliver mechanistic, mathematically rigorous, models integrating biochemical and pharmacological processes that result in toxicity to enhance the assessment of the risk posed to humans by drugs and other xenobiotics. The benefits of such ‘in silico’ models would be in enabling the rapid and robust prediction of the effects of compounds over a range of exposures, improving in vitro–in vivo correlations and the translation from preclinical species to humans. Systems toxicology models of organ toxicities that result in high attrition rates during drug discovery and development, or post-marketing withdrawals (e.g., drug-induced liver injury (DILI)) should facilitate the discovery of safe new drugs. Here, systems toxicology as applied to the effects of paracetamol (acetaminophen, N-acetyl-para-aminophenol (APAP)) is used to exemplify the potential of the approach.

Journal article

Hoyles L, Abbott JC, Holmes E, Nicholson JK, Dumas ME, Butcher SAet al., 2015, IMP: Imperial Metagenomics Pipeline for high-­throughput sequence data, Exploring Human Host-Microbiome Interactions in Health and Disease

Poster

Merrifield CA, Lewis MC, Berger B, Cloarec O, Heinzmann SS, Charton F, Krause L, Levin NS, Duncker S, Mercenier A, Holmes E, Bailey M, Nicholson JKet al., 2015, Neonatal environment exerts a sustained influence on the development of the intestinal microbiota and metabolic phenotype, ISME Journal, Vol: 10, Pages: 145-157, ISSN: 1751-7362

The postnatal environment, including factors such as weaning and acquisition of the gut microbiota, has been causally linked to the development of later immunological diseases such as allergy and autoimmunity, and has also been associated with a predisposition to metabolic disorders. We show that the very early-life environment influences the development of both the gut microbiota and host metabolic phenotype in a porcine model of human infants. Farm piglets were nursed by their mothers for 1 day, before removal to highly controlled, individual isolators where they received formula milk until weaning at 21 days. The experiment was repeated, to create two batches, which differed only in minor environmental fluctuations during the first day. At day 1 after birth, metabolic profiling of serum by 1H nuclear magnetic resonance spectroscopy demonstrated significant, systemic, inter-batch variation which persisted until weaning. However, the urinary metabolic profiles demonstrated that significant inter-batch effects on 3-hydroxyisovalerate, trimethylamine-N-oxide and mannitol persisted beyond weaning to at least 35 days. Batch effects were linked to significant differences in the composition of colonic microbiota at 35 days, determined by 16 S pyrosequencing. Different weaning diets modulated both the microbiota and metabolic phenotype independently of the persistent batch effects. We demonstrate that the environment during the first day of life influences development of the microbiota and metabolic phenotype and thus should be taken into account when interrogating experimental outcomes. In addition, we suggest that intervention at this early time could provide ‘metabolic rescue’ for at-risk infants who have undergone aberrant patterns of initial intestinal colonisation.

Journal article

Gray N, Lewis MR, Plumb RS, Wilson ID, Nichoson JKet al., 2015, High-Throughput Microbore UPLC-MS Metabolic Phenotyping of Urine for Large-Scale Epidemiology Studies, JOURNAL OF PROTEOME RESEARCH, Vol: 14, Pages: 2714-2721, ISSN: 1535-3893

Journal article

Afzal AM, Mussa HY, Turner RE, Bender A, Glen RCet al., 2015, A multi-label approach to target prediction taking ligand promiscuity into account, Journal of Cheminformatics, Vol: 7, ISSN: 1758-2946

BackgroundAccording to Cobanoglu et al., it is now widely acknowledged that the single target paradigm (one protein/target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous – it can interact with more than one target protein.In recent years, in in silico target prediction methods the promiscuity issue has generally been approached computationally in three main ways: ligand-based methods; target-protein-based methods; and integrative schemes. In this study we confine attention to ligand-based target prediction machine learning approaches, commonly referred to as target-fishing.The target-fishing approaches that are currently ubiquitous in cheminformatics literature can be essentially viewed as single-label multi-classification schemes; these approaches inherently bank on the single target paradigm assumption that a ligand can zero in on one single target. In order to address the ligand promiscuity issue, one might be able to cast target-fishing as a multi-label multi-class classification problem. For illustrative and comparison purposes, single-label and multi-label Naïve Bayes classification models (denoted here by SMM and MMM, respectively) for target-fishing were implemented. The models were constructed and tested on 65,587 compounds/ligands and 308 targets retrieved from the ChEMBL17 database.ResultsOn classifying 3,332 test multi-label (promiscuous) compounds, SMM and MMM performed differently. At the 0.05 significance level, a Wilcoxon signed rank test performed on the paired target predictions yielded by SMM and MMM for the test ligands gave a p-value < 5.1 × 10−94 and test statistics value of 6.8 × 105, in favour of MMM. The two models performed differently when tested on four datasets comprising single-label (non-promiscuous) compounds; McNemar’s tes

Journal article

Chekmeneva E, Correia G, Denes J, Gomez-Romero M, Wijeyesekera A, Perenyi DR, Koot Y, Boomsma C, Want EJ, Dixon PH, Macklon NS, Chan Q, Takats Z, Nicholson JK, Holmes Eet al., 2015, Development of nanoelectrospray high resolution isotope dilution mass spectrometry for targeted quantitative analysis of urinary metabolites: application to population profiling and clinical studies, Analytical Methods, Vol: 7, Pages: 5122-5133, ISSN: 1759-9679

An automated chip-based electrospray platform was used to develop a high-throughput nanoelectrospray high resolution mass spectrometry (nESI-HRMS) method for multiplexed parallel untargeted and targeted quantitative metabolic analysis of urine samples. The method was demonstrated to be suitable for metabolic analysis of large sample numbers and can be applied to large-scale epidemiological and stratified medicine studies. The method requires a small amount of sample (5 μL of injectable volume containing 250 nL of original sample), and the analysis time for each sample is three minutes per sample to acquire data in both negative and positive ion modes. Identification of metabolites was based on the high resolution accurate mass and tandem mass spectrometry using authentic standards. The method was validated for 8 targeted metabolites and was shown to be precise and accurate. The mean accuracy of individual measurements being 106% and the intra- and inter-day precision (expressed as relative standard deviations) were 9% and 14%, respectively. Selected metabolites were quantified by standard addition calibration using the stable isotope labelled internal standards in a pooled urine sample, to account for any matrix effect. The multiple point standard addition calibration curves yielded correlation coefficients greater than 0.99, and the linear dynamic range was more than three orders of magnitude. As a proof-of-concept the developed method was applied for targeted quantitative analysis of a set of 101 urine samples obtained from female participants with different pregnancy outcomes. In addition to the specifically targeted metabolites, several other metabolites were quantified relative to the internal standards. Based on the calculated concentrations, some metabolites showed significant differences according to different pregnancy outcomes. The acquired high resolution full-scan data were used for further untargeted fingerprinting and improved the differentiation of

Journal article

Guenther S, Muirhead LJ, Speller AVM, Golf O, Strittmatter N, Ramakrishnan R, Goldin RD, Jones E, Veselkov K, Nicholson J, Darzi A, Takats Zet al., 2015, Spatially Resolved Metabolic Phenotyping of Breast Cancer by Desorption Electrospray Ionization Mass Spectrometry, CANCER RESEARCH, Vol: 75, Pages: 1828-1837, ISSN: 0008-5472

Journal article

Elliott P, Posma JM, Chan Q, Garcia-Perez I, Wijeyesekera A, Bictash M, Ebbels TMD, Ueshima H, Zhao L, van Horn L, Daviglus M, Stamler J, Holmes E, Nicholson JKet al., 2015, Urinary metabolic signatures of human adiposity, SCIENCE TRANSLATIONAL MEDICINE, Vol: 7, ISSN: 1946-6234

Journal article

O'Keefe SJ, Li JV, Lahti L, Ou J, Carbonero F, Mohammed K, Posma JM, Kinross J, Wahl E, Ruder E, Vipperla K, Naidoo V, Mtshali L, Tims S, Puylaert PG, DeLany J, Krasinskas A, Benefiel AC, Kaseb HO, Newton K, Nicholson JK, de Vos WM, Gaskins HR, Zoetendal EGet al., 2015, Fat, fibre and cancer risk in African Americans and rural Africans., Nat Commun, Vol: 6

Rates of colon cancer are much higher in African Americans (65:100,000) than in rural South Africans (<5:100,000). The higher rates are associated with higher animal protein and fat, and lower fibre consumption, higher colonic secondary bile acids, lower colonic short-chain fatty acid quantities and higher mucosal proliferative biomarkers of cancer risk in otherwise healthy middle-aged volunteers. Here we investigate further the role of fat and fibre in this association. We performed 2-week food exchanges in subjects from the same populations, where African Americans were fed a high-fibre, low-fat African-style diet and rural Africans a high-fat, low-fibre western-style diet, under close supervision. In comparison with their usual diets, the food changes resulted in remarkable reciprocal changes in mucosal biomarkers of cancer risk and in aspects of the microbiota and metabolome known to affect cancer risk, best illustrated by increased saccharolytic fermentation and butyrogenesis, and suppressed secondary bile acid synthesis in the African Americans.

Journal article

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