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Amyloid Beta: from Pre-Analytical Factors to Disease Mechanisms

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Amyloid Beta: from Pre-Analytical Factors to Disease Mechanisms Amyloid beta: from pre-analytical factors to disease mechanisms Jamie Toombs UCL PhD Neuroscience I, Jamie Toombs, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. 1 Abstract Background Biomarkers are powerful tools for interrogating the basic science of disease processes, in the clinical detection of disease states, and as both targets and endpoints in therapeutic strategy. Amyloid beta (Aβ) is a core biomarker for Alzheimer’s disease (AD), but measurement variation between sites and experiments limits its potential. Furthermore, although the role of brain Aβ accumulation early in AD is extremely well attested, the biological mechanisms underlying this remain poorly understood. Methods To contribute to the development of treatments for AD patients and those at risk, this thesis set out to identify important pre-analytical confounding factors in Aβ measurement, strategies to mitigate them, and identify disease relevant patterns of Aβ peptide production in human CSF and an induced pluripotent stem cell-derived cortical neuron model of familial AD (fAD). Results A series of experiments demonstrated the importance of sample surface exposure to the measurement of Aβ peptides and tau. The volume at which samples are stored and iterative contact with fresh surfaces had profound effect on Aβ, but not tau, with greater surface exposure resulting in depletion of Aβ concentration. The mechanism was demonstrated to be protein surface adsorption. Importantly, the different Aβ peptides did not absorb to polypropylene to the same extent; Aβ42 concentration decreased proportionally more with surface exposure treatment than Aβ40 and Aβ38. It was observed that the addition of a non-ionic surfactant (Tween 20) to samples significantly mitigated the effect of surface exposure treatments on Aβ peptides and tau. However, the use of this additive did not meaningfully improve variability when sample storage conditions were standardised. Furthermore, variances in clinic to laboratory temperature and time interval did not significantly affect Aβ or tau concentration. Validation of an in vitro model of fAD was conducted. Experiment identified the use of Aβ ratios as a robust method for normalising data variability between and within cell lines over extended time periods. Furthermore, comparison of paired CSF, cell media, cell lysates, and post-mortem cortical 2 tissue from the same individual demonstrated physiologically consistent patterns of Aβ ratios across sample types. Finally, comparison of multiple fAD mutation and control cell lines demonstrated quantitative and qualitative differences in secreted Aβ. APP V717I neurons increased secretion of Aβ42 and Aβ38 relative to Aβ43 and Aβ40. PSEN1 mutations increased secretion of longer Aβ peptides relative to shorter Aβ peptides, with mutation specific differences such as greatly increased Aβ43 in PSEN1 R278I. Conclusions This work demonstrated several novel considerations in the use of Aβ peptides as biomarkers for AD. Data principally highlight the importance of Aβ ratios to AD biomarker research, the necessity of controlling pre-analytical sample surface exposure intended for the measurement of ‘sticky’ protein biomarkers such as Aβ peptides, and the validity of iPSC-derived neuronal models for exploring the production of Aβ in AD and health. 3 Impact statement This work formed part of an ongoing movement to improve the quality and reliability of biomarker data in the field of Alzheimer’s research. Contribution was to two main areas: the effect of sample surface exposure on the Aβ content of CSF, and the production of Aβ peptides in fAD patient neurons. Concerning the former, experiments identified and characterised a number of previously under-appreciated aspects of CSF and cell media storage and handling (storage volume, consecutive aliquoting, the effect of using a manometer), which inspired investigations by other groups and contributed to proposals for “gold standard” CSF collection protocols. Furthermore, data highlighted the potential of sample additives (Tween 20) to mitigate surface adsorption of vulnerable proteins such as Aβ peptides. This generated a certain amount of interest from ELISA manufacturing companies. Concerning Aβ production in vitro, different fAD genotypes were shown to produce distinct ratios of Aβ peptides, supporting a growing understanding of APP proteolysis mechanisms that will inform understanding of past drug failure and future drug development. Additionally, the notion of Aβ as a biomarker was expanded with presentation of the concept of Aβ peptide ratios as biomarkers for γ-secretase activity. This thesis also represents one of only two studies to compare iPSC-derived neurons with tissues from the same individual, in this case brain tissue homogenate and CSF in an individual with an APP V717I mutation. Finally, work culminated in six original publications to Clinical Chemistry and Laboratory Medicine, Alzheimer’s & Dementia, Alzheimer’s Research & Therapy, and the Journal of Alzheimer’s disease, with a seventh under revision at Molecular Psychiatry. 4 Acknowledgements I would like to gratefully thank my supervisors Henrik Zetterberg, Selina Wray, Amanda Heslegrave and John Hardy for their expert guidance, zen-like patience, inordinate generosity, and boundless enthusiasm. This has been an amazing experience and privilege. I would additionally like to thank the many outstanding people I have had the good fortune to work with and beside over the course of this project. Of these, Ross Paterson, Charles Arber, Christopher Lovejoy, Julia Ravey, Lotta Agholme, and Petra Bergtröm deserve especial credit. Their partnership was integral to designing, conducting and reporting the results of the many experiments we conducted together. Furthermore, I would like to thank the many mentors who imparted their knowledge and skills so generously: Michael Lunn, Viki Worthington, Miles Chapman, Andrew Church, Neghat Lakdawala, Rosmary Monero, Donna Grant, and Michael Chou, Axel Petzold, Jonas Söderblom, Erik Portelius, and Ulf Andreason, Michael Duchen and Gauri Bosale. Extra-special thanks go to Henrietta Wellington, Martha Foiani, Carolin Heller, and Elena Veleva of the UCL DRI Biomarker Research Laboratory (Formerly the Leonard Wolfson Biomarker Laboratory) for their incredible support and friendship. Finally, what acknowledgement is complete without a recognition of family? This is for my parents Margaret, Richard, and Mark, and my dearest loves Diana and little Jasper. Thank you so much for your love and support. 5 Contents Abstract .................................................................................................................................................. 2 Impact statement ................................................................................................................................... 4 Acknowledgements ................................................................................................................................ 5 Abbreviations ....................................................................................................................................... 14 1 Background ................................................................................................................................... 15 1.1 Project Aims .......................................................................................................................... 15 1.2 Alzheimer’s disease research in context ............................................................................... 15 1.3 Alzheimer’s disease ............................................................................................................... 18 1.3.1 Alzheimer’s disease: Subcategories .............................................................................. 21 1.4 Amyloid beta biology ............................................................................................................ 23 1.4.1 Amyloid Precursor Protein............................................................................................. 24 1.4.1.1 Amyloid Precursor Protein Genetics ......................................................................... 24 1.4.1.2 Amyloid Precursor Protein Isoforms ......................................................................... 25 1.4.1.3 Amyloid Precursor Protein translation and transport .............................................. 26 1.4.1.4 Amyloid Precursor Protein Function ......................................................................... 29 1.4.2 Amyloid precursor protein processing........................................................................... 31 1.4.2.1 α-secretase ................................................................................................................ 32 1.4.2.2 β-secretase ................................................................................................................ 33 1.4.2.3 BACE2: an alternative -secretase ............................................................................ 34 1.4.2.4 γ-Secretase ................................................................................................................ 35 1.4.2.5 The tripeptide hypothesis ......................................................................................... 38 1.4.2.6 γ-Secretase loss of function .....................................................................................
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