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Using Mathematical Modelling and Electrochemical Analysis to Investigate Age￿associated Disease

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Using Mathematical Modelling and Electrochemical Analysis to Investigate Age￿associated Disease Using Mathematical Modelling and Electrochemical Analysis to Investigate AgeAssociated Disease Item Type Thesis or dissertation Authors Morgan, Amy Citation Morgan, A. E. (2019). Using Mathematical Modelling and Electrochemical Analysis to Investigate AgeAssociated Disease. (Doctoral dissertation). University of Chester, United Kingdom. Publisher University of Chester Rights Attribution-NonCommercial-NoDerivatives 4.0 International Download date 04/10/2021 12:00:32 Item License http://creativecommons.org/licenses/by-nc-nd/4.0/ Link to Item http://hdl.handle.net/10034/622177 Using Mathematical Modelling and Electrochemical Analysis to Investigate Age‐Associated Disease Thesis submitted in accordance with the requirements of the University of Chester for the degree of Doctor of Philosophy By Amy Elizabeth Morgan MRes, BSc (Hons), PGCertLTHE, FHEA March 2019 University of Chester Thornton Science Park Pool Lane Ince Chester CH2 4NU 1 Declaration The material being presented for examination is my own work and has not been submitted for an award of this or another HEI except in minor particulars which are explicitly noted in the body of the thesis. Where research pertaining to the thesis was undertaken collaboratively, the nature and extent of my individual contribution has been made explicit. Signed: …………………………………………………….. Date: ……………………………………………………….. 2 Acknowledgements I would like to thank my family, friends, colleagues, and supervisory team for their continued support throughout this journey. I would also like to thank the University of Chester for internally funding this PhD, without which, this would not have been possible. Further thanks go to the Institute of Medicine, University of Chester, for donating the MCF‐7 cells and providing support with cell culturing. 3 Abbreviations ABC‐A1: Adenosine triphosphate binding cassette subfamily A member 1 ABCG5/G8: Adenosine triphosphate binding cassette subfamily G5/G8 AC: Alternating current ACAT1: Acetyl‐coenzyme A: cholesterol acetyltransferase 1 ACAT2: Acetyl‐coenzyme A: cholesterol acetyltransferase 2 AD: Alzheimer’s disease Akt (PKB): Protein kinase B apo: Apolipoprotein ASBT: Apical sodium dependent bile acid transporter ATP: Adenosine triphosphate Au‐RDE: Gold rotating disk electrode Au‐SPE: Gold screen printed electrode BMI: Body mass index BSEP: Bile salt export pumps BSH: Bile salt hydrolase CAD: Coronary artery disease CALERIE: Comprehensive Assessment of Long‐Term Effects of Reducing Calorie Intake CEH: Cholesterol ester hydrolase CETP: Cholesteryl ester transfer protein 4 CHD: Coronary heart disease CKK: Cholecystokinin CpG: Cytosine‐guanine dinucleotide CR: Caloric restriction CRP: C‐reactive protein ctDNA: Circulating tumour DNA CV: Cyclic voltammetry/voltammogram CV50: Cyclic voltammetry/voltammogram at 50mV/s CV200: Cyclic voltammetry/voltammogram at 200mV/s CVD: Cardiovascular disease CYP7AI: Cholesterol 7‐alpha‐hydroxylase DASH: Dietary Approaches to Stop Hypertension DCE: Dietary cholesterol esters ∆: Peak to peak separation DFC: Dietary free cholesterol DNA: Deoxyribonucleic acid dNTPs: Deoxynucleotide triphosphates DPV: Differential pulse voltammetry dsDNA: Double stranded DNA EIS: Electrochemical impedance spectroscopy 5 EMEM: Eagle’s minimal essential medium EN1: engrailed homeobox 1 gene : Peak oxidation potential against Ag/AgCl reference electrode : Peak reduction potential against Ag/AgCl reference electrode FBA: Foetal bovine serum FFA: Free fatty acid GH: Growth hormone FoxO3: Forkhead box O 3 HCBA: Hepatic conjugated bile acid HCE: Hepatic cholesterol esters HDL: High density lipoprotein HDL‐C: High density lipoprotein cholesterol HFC: Hepatic free cholesterol HL: Hepatic lipase HLDLRs: Hepatic low density lipoprotein receptors HMG‐CoA: 3‐hydroxy‐3‐methylglutaryl CoA HSL: Hormone sensitive lipase HSP: Heat shock protein HUBA: Hepatic unconjugated bile acid ICBA: Ileocytic conjugated bile acid 6 IDL: Intermediate density lipoprotein IDL‐C: Intermediate density lipoprotein cholesterol IHD: Ischemic heart disease : Current before pulse : Current at pulse : Peak anodic current : Peak oxidation current : Peak reduction current IUBA: Ileocytic unconjugated bile acid KO: Knock out LCAT: Lecithin‐cholesterol acyltransferase LCBA: (Intestinal) Lumen conjugated bile acid LDL: Low density lipoprotein LDL‐C: Low density lipoprotein cholesterol LDLr: Low density lipoprotein receptor LPL: Lipoprotein lipase LRP: Low density lipoprotein receptor‐related protein LUBA: (Intestinal) Lumen unconjugated bile acid LVC: Longevity village communities LXRα: Liver X receptor α 7 MCP‐1: Monocyte chemotactic protein 1 MTHFR: Methylenetetrahydrofolate reductase mTOR: Mechanistic target of rapamycin mTORC1: Mechanistic target of rapamycin complex 1 mTORC2: Mechanistic target of rapamycin complex 2 MTP: Microsomal triglyceride transfer protein MUFA: Monounsaturated fatty acid ndHDL: Nascent HDL NEAA: Non‐essential amino acids NO: Nitric oxide NPC1L1: Niemann‐pick C1‐like 1 ODE: Ordinary differential equation OTU: Operational taxonomic unit ox‐HDL: Oxidised high density lipoprotein ox‐LDL: Oxidised low density lipoprotein PAMP: Pathogen associated pattern PBS: Phosphate buffered saline PCE: Peripheral cholesterol esters PCR: Polymerase chain reaction PDGF: Mitogen platelet derived growth factor 8 P‐DMRs: Prenatal malnutrition‐associated differentially methylated regions PFC: Peripheral free cholesterol PH: Pulse height PI3K: Phosphatidylinositol 3‐kinase PLTP: Phospholipid transfer protein PSCK9: Proprotein convertase subtilisin kexin‐9 PUFA: Polyunsaturated fatty acid PW: Pulse width RCT: Reverse cholesterol transport Rct: Charge transfer resistance Reg3g: Regenerating islet‐derived 3 gamma ROS: Reactive oxygen species RSD: Relative standard deviation SA: Sensitivity analysis SAH: S‐adenosyl homocysteine SAM: S‐adenosyl methionine SBML: Systems biology markup language SCS: Seven Countries Study SD: Standard devation SFA: Saturated fatty acid 9 SH: Step height Si: Sensitivity coefficient Sirt6: Sirtuin 6 SNP: Single nucleotide polymorphism SR‐BI: Scavenger receptors of class B type I SREBP‐2: Sterol‐regulatory element binding protein 2 ssDNA: Single stranded DNA ST: Step time TC: Total cholesterol TG: Triglyceride TNF‐α: Tumour necrosis factor α T2DM: Type II diabetes mellitus UTC: Urbanised town communities VLDL: Very low density lipoprotein VLDL‐C: Very low density lipoprotein cholesterol WGA: Whole genome amplified Z: Impedance 10 Abstract People are living longer. With this rise in life expectancy, a concomitant rise in morbidity in later life is observed; with conditions including cardiovascular disease (CVD), and cancer. However, ageing and the pathogenesis of age related disease, can be difficult to study, as the ageing process is a complex process, which affects multiple systems and mechanisms. The aim of this research was two‐fold. The first aim was to use mathematical modelling to investigate the mechanisms underpinning cholesterol metabolism, as aberrations to this system are associated with an increased risk for CVD. To better understand cholesterol from a mechanistic perspective, a curated kinetic model of whole body cholesterol metabolism, from the BioModels database, was expanded in COPASI, to produce a model with a broader range of mechanisms which underpin cholesterol metabolism. A range of time course data, and local and global parameter scans were utilised to examine the effect of cholesterol feeding, saturated fat feeding, ageing, and cholesterol ester transfer protein (CETP) genotype. These investigations revealed: the model behaved as a hypo‐responder to cholesterol feeding, the robustness of the cholesterol biosynthesis pathway, and the impact CETP can have on healthy ageing. The second aim of this work was to use electrochemical techniques to detect DNA methylation within the engrailed homeobox 1 (EN1) gene promoter, which has been implicated in cancer. Hypermethylation of this gene promoter is often observed in a diseased state. Synthetic DNA, designed to represent methylated and unmethylated variants, were adsorbed onto a gold rotating disk electrode for electrochemical analysis by 1) electrochemical impedance spectroscopy (EIS), 2) cyclic voltammetry (CV) and 3) differential pulse voltammetry (DPV). The technique was then applied to bisulphite modified and asymmetrically amplified DNA from the breast cancer cell line MCF‐7. Results indicated that electrochemical techniques could detect DNA methylation in both synthetic and cancer derived DNA, with EIS producing superior results. These non‐traditional techniques of studying age related disease were effective for the investigation of cholesterol metabolism and DNA methylation, and this work highlights how these techniques could be used to elucidate mechanisms or diagnose/monitor disease pathogenesis, to reduce morbidity in older people. 11 Contents Chapter 1 Introduction ......................................................................................................................... 21 1.1 PART 1: Cholesterol metabolism and ageing .............................................................................. 26 1.1.1 Introduction ......................................................................................................................... 26 1.1.2 Overview of cholesterol metabolism ................................................................................... 32 1.1.3 Impact of ageing on cholesterol metabolism .....................................................................
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