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A Metabonomics Study of the Modulation of Lipid and Bile Acid Metabolism by the Gut Microbiota and Consequences on Obesity and Fatty Liver Disease

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A Metabonomics Study of the Modulation of Lipid and Bile Acid Metabolism by the Gut Microbiota and Consequences on Obesity and Fatty Liver Disease A metabonomics study of the modulation of lipid and bile acid metabolism by the gut microbiota and consequences on obesity and fatty liver disease Thesis submitted by Magali H. SARAFIAN Supervisors: Dr. Marc Emmanuel Dumas & Prof. Elaine Holmes For thesis submitted for the degree of Doctor of Philosophy and Diploma of Imperial College London Section of Biomolecular Medicine Division of Computational Systems Medicine Department of Surgery and Cancer Imperial College London 2016 ABSTRACT Obesity and fatty liver disease are characterised by an imbalance between energy intake and energy expenditure. Genetic and environmental factors strongly contribute to impaired energy homoeostasis and can trigger metabolic disorders such as hyper- tension, hyperglycaemia and hypercholesterolemia which are associated with type 2 diabetes, cardiovascular diseases and metabolic syndrome. The mechanisms under- pinning obesity and its co-morbidity are poorly understood but dysregulation of lipid metabolism is likely to contribute and can be studied by metabolic profiling using UPLC-MS. Moreover, the host-gut axis has a strong influence on obesity and fatty liver disease development by modulation of metabolic pathways. The analytical qual- ity of the metabolic profiling methods is critical to understand the aetiopathogenesis of obesity. Hence the aim of this PhD project is to investigate lipid metabolism in obesity and fatty liver disease. I developed and optimised a strategy for characterising the global lipid profile of plasma based on isopropanol precipitation and supplemented this with the development of a targeted assay for obtaining in depth profile of bile acids (BAs) (n=145) since BAs are known to play a specific role in obesity and fatty liver disease. Furthermore, BAs can provide insight into the role of the gut microbiota in obesity as they are metabolised by the gut microbiota. The analytical pipeline for lipid and BA analysis are subsequently applied for two human clinical studies. i) The metabolic signatures associated with subcutaneous and visceral obesity were investigated. A significant increase of LPC (16:0), unconjugated BAs and sulphated BAs with a decrease of PC (16:0/20:3), taurine conjugates was observed in visceral obesity compared to subcutaneous obesity. ii) Subtle disease 1 progression in fatty liver disease, NAFLD and NASH was evaluated. A significant increase of triacylglycerols, hyocholic acid and tauro-conjugated BAs with a decrease of phosphocholines were observed in NASH compared to NAFLD. This phD project illustrated that metabonomic instrumentations (UPLC-MS and UPLC-MS/MS) are non-invasive and powerful analytical techniques to diagnose obe- sity and fatty liver disease. In addition, metabonomic analysis of urine and blood samples may offer the possibility to optimise individual diagnosis and management of patients with obesity and fatty liver disease. 2 STATEMENT OF ORIGINALITY I certify that this thesis, and the research to which it refers, are the product of my own work, and that any ideas or quotations from the work of other people, published or otherwise, are fully acknowledged in accordance with the standard referencing prac- tices of the discipline. I would like to mention the contributions of Mathieu Gaudin and Matthew Lewis for the BA targeted assay presented in chapter 3. The design of the BA sulfation reaction and optimisation was developed with the help of Dr Mathieu Gaudin and BA UPLC method was optimised with the help of Dr Matthew Lewis. Magali H. SARAFIAN, January 2016 3 COPYRIGHT DECLARATION The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work. 4 ACKNOWLEDGEMENTS I would like to thank my supervisors Dr Marc E. Dumas and Prof Elaine Holmes for your guidance, patience and trust. You were constant support throughout my ups and downs during my PhD thesis and have been inspirational mentors and teachers. I would like also to thank the head of Surgery and Cancer; Prof. Jeremy Nicholson who contributed to the success of my PhD projects. I acknowledge the support of Nestl´e for funding this PhD project. Thank you to Dr Francois-Pierre Martin, Dr Serge Rezzi, Dr Sebastiano Collino and Dr Sunil Kochhar for their advice on projects and interpretation of data analysis. I am especially grateful to Dr Matthew Lewis, a research collaborator and MS mentor who has become a great friend and a constant source of positivity. I would like to thanks also your team at the NPC, you are amazing source of knowledge and fun. I would like thank the people that I worked with in Computational and System Medicine at Imperial College, I had great time working with you, Mathieu, Julien, Laura, Ana, Andrea, Badrin, and others that I cannot mention as it would be too long. I would like also to thank my friends from Norwich, place where my interest for metabonomics started, Dr Gwenaelle Le Gall, Mark Philo, Ian Colquhoun, Rob and Ruth. Special mention to the ”London Family” and especially to Lea, a fantastic friend who has also helped me through many moments of panic! Including Anita, Panos, Claire, Abby, Gaby, Kathy, Alex F., Evie, Alex P., Dina, Harris, Vassilli, Michael, Maria, Nandish, Anthony, Ebony, Paul and Florian. I would like to thank my old friends from Casteljaloux, Bordeaux and Toulouse. I would like to dedicate my thesis to my family, Mum, Dad, my twin Romain, Ana¨ıs, Jean-Maurice, Marie-Aude, Adela¨ıde, Cyril, Tha¨ıs, Stephane, Nadine, Maxime, H´el`ene, Fr´ed´eric, Sophie and Bastien. I will not forget my nieces and nephews, Elisa, Colin, Olivia, Maud and Alexis, what a great little team taking on the world and making it better! And my favourite little friends, Toulouse, Violette, Winnie and my dear Undavril. Especially thank you to my parents, Catherine and Jean who believed in me and gave me the opportunity to have this incredible life in England. Thank you Dad you always have been a source of inspiration. Dear Julien thank you for being there through it all you are a constant source of support. I really couldn’t have done it without you! 5 TABLE OF CONTENTS Abstract . .1 Statement of originality . .3 Copyright declaration . .4 Acknowledgements . .5 Table of contents . .6 List of figures . 11 List of tables . 13 1 Introduction 18 1.1 Obesity, non-alcoholic fatty liver disease and metabolic syndrome . 18 1.1.1 Epidemiology . 18 1.1.2 Obesity to NAFLD disease and metabolic syndrome . 20 (a) Aetiology . 20 (b) Pathogenesis . 20 (c) Clinical diagnosis . 22 1.2 Classification and biochemistry of lipids based on Lipidmaps . 24 1.2.1 Fatty acyls . 25 1.2.2 Glycerolipids . 26 1.2.3 Glycerophospholipids . 26 1.2.4 Sphingolipids . 27 1.2.5 Sterol lipids . 28 1.2.6 Other lipid classes: prenols, sacccharolipids and polyketides . 31 1.3 Lipid metabolism . 32 1.3.1 Lipids and lipoprotein metabolism . 32 1.3.2 Role of the gut microbiota and lipid metabolism . 33 1.3.3 BA metabolism and the gut microbiota . 34 1.4 Metabonomics . 41 1.5 Thesis outlines and aims . 42 2 The metabonomic approach 44 2.1 Sample preparation . 45 2.1.1 Sample type . 45 2.1.2 Storage . 46 2.1.3 Sample preparation: Lipid extractions . 46 2.2 UPLC-MS analytical techniques . 48 2.2.1 LC . 48 (a) Theory of chromatography . 48 (b) Column chemistry in LC . 49 (c) Mobile phase in LC . 50 2.2.2 Mass spectrometry . 50 6 (a) Ionisation . 50 (b) Analysers . 51 (c) Detector . 53 2.3 The metabonomic run strategy . 54 2.4 Data pre-processing . 55 2.4.1 Peak detection . 55 2.4.2 Peak alignment . 56 2.4.3 Peak filtering . 56 2.4.4 Normalisation . 56 2.5 Data analysis . 57 2.5.1 Chemometrics . 57 2.5.2 Structural identification . 58 3 An Objective Set of Criteria for Optimisation of Sample Prepara- tion Procedures for Ultra-High Throughput Untargeted Blood Plasma Lipid Profiling by UPLC-MS 60 3.1 Introduction . 60 3.2 Material and methods . 63 3.2.1 Materials . 63 3.2.2 Sample preparation for UPLC-MS: Precipitation . 63 3.2.3 Sample preparation for UPLC-MS: Extraction . 63 3.2.4 Protein quantification . 65 3.2.5 Ultra Performance Liquid Chromatography . 66 3.2.6 Lipid profiling by UPLC-Q-ToF Mass Spectrometry . 66 3.2.7 Structural identification . 67 3.2.8 MS data pre-processing . 67 3.2.9 Multivariate statistical analysis . 67 3.2.10 Univariate statistical analysis . 68 3.2.11 Lipid recovery . 68 3.3 Results . 69 3.3.1 Simplicity of protocols . 69 3.3.2 Protein removal efficiency . 71 3.3.3 Lipid coverage . 72 3.3.4 Repeatability . 77 3.3.5 Lipid recovery . 78 3.4 Discussion . 82 3.5 Conclusion . 84 4 UPLC-MS/MS targeted method for bile acids 85 4.1 Introduction . 85 4.2 Materials and methods . 89 4.2.1 Materials . 89 4.2.2 Collection of human plasma for targeted analysis . 89 4.2.3 Sample preparation for targeted analysis of human pre-/ post- prandial study . 89 4.2.4 UPLC-MS profiling and MS/MS conditions . 90 4.2.5 Optimisation of BA sulfation . 95 4.2.6 Purification of sulfated BAs . 95 4.2.7 Optimisation of MRM transitions and SIR . 95 7 4.2.8 Method validation . 96 4.2.9 MS data pre-processing .
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