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Molecular Neuropathology in Alzheimer's Disease

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Molecular Neuropathology in Alzheimer's Disease Molecular Neuropathology in Alzheimer’s Disease Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Carol Huseby, M.S. Graduate Program in Biophysics The Ohio State University 2018 Dissertation Committee: Jeffrey Kuret, Ph.D., Advisor Ralf Bundschuh, Ph.D. Kari Hoyt, Ph.D. Sherwin Singer, Ph.D. Copyrighted by Carol Huseby 2018 Abstract Alzheimer’s disease affects one in ten Americans and incidences are on the increase while other leading causes of disease are declining. Alzheimer’s disease is defined by two proteinaceous lesions in brain, extracellular Aβ plaques and intracellular tau neurofibrillary tangles. Tau protein is a microtubule-associated protein found abundantly expressed in neurons. Its function is to stabilize and promote microtubules and neurite outgrowth. In Alzheimer’s disease, tau protein is hyperphosphorylated and dissociates from microtubules to form aggregate filaments inside neurons disrupting the transport along axons within neurons. A remarkable aspect of the AD disease progression is the range of brain regions that are affected in a systematic, sequential manner maintaining a predictable distribution pattern of intraneuronal tau lesions varying little between individuals and an extended prodromal period. The progression of neurofibrillary tau tangle lesions in disease behaves like a prion, propagating neuron-to-neuron recruiting new tau molecules to mis-fold and continue the disease process. The mechanisms of tau protein aggregation are not clear. In this thesis I explore tau protein aggregation using biochemical methods and mathematical models and/or analysis to help clarify the aggregation mechanisms of tau protein leading to potential therapeutic strategies or targets. ii Dedication I dedicate this dissertation work to my two wonderful children I love very much, John and Emry, who supported my decision to return to school and were my best cheerleaders throughout my doctorate career. I dedicate this work to my family, my magnificent brothers and sisters; Mike, Ian, Shonda, Wade, Kyle, Adam, Jed, Eileen, Peter, Rebecca, Sam, Audrey, and Susan. I especially dedicate to my parents, who have always let us be ourselves and supported their children’s endeavors. Although my father, Donald Gene, has passed since I began my doctorate career, my mother, Marian, continues to provide me with encouragement and praise. iii Acknowledgements I would like to acknowledge my advisor and my committee members for their expertise and generous time. A special thanks to my advisor, Dr. Jeff Kuret, for countless hours of training, encouragement and patience throughout my entire doctoral career, as well as, thanks for the support and opportunity to conduct interdisciplinary research in your laboratory. Thank you Dr. Ralf Bundschuh, Dr. Kari Hoyt, and Dr. Sherwin Singer for serving on my committee. I thank the OSU Campus Microscopy and Imaging Facility for access to electron and fluorescent microscopy resources as well as the OSU Neuroscience Imaging Core for use of the confocal microscopes. I thank the Targeted Metabolomics Laboratory at The Ohio State University for access to LC–MS/MS equipment (funded by the Translational Plant Sciences Targeted Investment in Excellence). I also thank Jean Christophe Cocuron and Dr. Anna Alonso, at the BioDiscovery Institute, University of North Texas for LC-MS/MS analysis of samples. This work was supported by Public Health Service grants NS77441 and AG54018 and also by NIH grant AG14452. iv Vita 2012-2018 Graduate Research Associate, Interdisciplinary Biophysics Graduate Program, Biological Chemistry and Pharmacology, The Ohio State University, Columbus, OH, Ph.D. Candidate (defense 2018). 2010-2012 Master of Science in Applied Mathematics, University of Washington, Seattle, WA. 2006-2010 Bachelor of Science in Physics, University of Washington, Seattle, WA. Bachelor of Science in Biology (Physiology), University of Washington, Seattle, WA. Publications 1. A liquid chromatography tandem mass spectroscopy approach for quantification of protein methylation stoichiometry. (2018) G. Cooper-Olson**, C.J. Huseby**, C.N. Chandler, J.C. Cocuron, A. Alonso, J. Kuret, Analytical Biochemistry Feb 2, 545, 72-77. **equal contribution 2. Structural determinants of Tau aggregation inhibitor potency. (2013) K. Schafer, K. Cisek, C.J. Huseby, E. Chang, J. Kuret; J. Biol. Chem. 288 (45), 32599-32611. 3. Analyzing tau aggregation with electron microscopy. (2016) C.J. Huseby and J. Kuret; Meth. Mol. Bio.1345, 101-12. 4. Structure and mechanism of action of tau aggregation inhibitors. (2014) K. Cisek, G. Cooper, C.J. Huseby, J. Kuret; Current Alzheimer Research. 11(10), 918-27. Field-of-Study Major Field: Biophysics v Table of Contents Abstract…………………………………………………….…………………..…………ii Dedication…………………………………………….…………………….….…….…..iii Acknowledgments……………………………………..…………..…………..…...…….iv Vita.…………………………………………………………………………..….………..v List of Tables…………………………………………………………….….…..………vii List of Figures………………………………………………………….……..….……..viii List of Abbreviations…………………………………………………………..…….…...x Chapter 1. Introduction…………………………………………………………..….……1 Chapter 2. Analyzing tau aggregation with electron microscopy……….…….….…..…26 Chapter 3. The role of annealing and fragmentation in tau aggregation dynamics.…......43 Chapter 4 Liquid chromatography tandem mass spectroscopy approach……..…….…..72 Chapter 5 LC-MS/MS measure of stoichiometry tau methylation in human samples…..91 Chapter 6 Toward a more robust gene expression signature for AD…………..……..….104 Chapter 7 Conclusions and perspectives……………..…………………….……..….…117 References………………………………………………………………………………121 vi List of Tables Table 1 Percent affected with Alzheimer’s dementia in U.S. by age group…….………..7 Table 2 List of diseases associated with tau protein lesions…………………….……....23 Table 3 Model parameters…………………………………….……………….….……..67 Table 4 Mass spec parameters and calibration of amino acids………………….………82 Table 5 Human tissue case demographics…………………………………..….….…..102 vii List of Figures Figure 1 Genetic loci of AD risk factors.……………………………….…………………4 Figure 2 Confocal images of Aβ plaques and neurofibrillary tangles……………..….….11 Figure 3 Model of the progression of dynamic biomarkers in AD………………………13 Figure 4 Tau pathology looks like neuron-to-neuron propagation………………………17 Figure 5 Cross-sectional study of disease progression in human brain tissue….……..…19 Figure 6 MAPT gene and tau protein schematics……………………….……………….21 Figure 7 Technique for applying tau samples to grids…………………………….….….31 Figure 8 Electron micrograph of tau filaments binned as function of length……………33 Figure 9 Graphical depiction of parameter estimations…………………….………..…..35 Figure 10 Immunogold labeling of epitope-tagged tau filaments…………..……..……..38 Figure 11 Aggregation model scheme……………………………………………..…….46 Figure 12 Construct comparison of aggregation propensity and morphology…….…….51 Figure 13 Immunogold labeling controls……………………………………….………..52 Figure 14 Direct visualization of tau filament annealing………………………….….….54 Figure 15 Annealing time-course approximates second-order kinetics……..………..….56 Figure 16 mathematical model of tau aggregation…………………………….……..….59 Figure 17 Model fit to protomer concentration and length distributions………..….……61 Figure 18 Global sensitivity analysis of NEAFS model………………………..……..…63 Figure 19 Model fits to protomer concentration time-series…………………..….……..65 Figure 20 Model fits to length distribution time-series………………………………….66 Figure 21 Tau filament annealing fragmentation equilibrium is length dependent…..….70 Figure 22 Ion chromatograms for amino acid standards……………………………..….80 Figure 23 Quantification of Lys methylation stoichiometry in tau protein……………...85 Figure 24 Quantification of methylation stoichiometry in protein standards……………88 viii Figure 25 Western blots and silver stain of human tissue samples………………..……..98 Figure 26 Bulk methyl Lys and methyl Arg in human tau preparations……….……....100 Figure 27 Methylation of tau protein in HEK293 cells…………………….………..…103 Figure 28 Cohort distributions NCBI-GEO database accession number GSE44772…..108 Figure 29 Comparison volcano plots for change of expression in AD…………………110 Figure 30 Fraction of average trends in covariates……………………………………..112 Figure 31 Aβ protease expression changes in AD…………………………….………..113 Figure 32 Methyltransferases and de-methylases expression changes in AD…….……115 ix List of Abbreviations 1meK, 2meK and 3meK, Nᵋ-(methyl)-, Nᵋ-(dimethyl)-, and Nᵋ-(trimethyl)-L-lysine, respectively; 1meR, NG-Methyl-L-arginine; Aβ, Amyloid-beta; AD, Alzheimer’s disease/dementia; CR, cerebellum; CSF, Cerebral spinal fluid; CJD, Creutzfeldt-Jacob disease; ADMA, NG,NG′-Dimethyl-L-arginine; APOE, Apolipoprotein E; APP, Amyloid precursor protein; DE, differential expression; DLB, Dementia with Lewy bodies; DS, Down syndrome; ECL, Electrochemiluminescence; EOAD, Early-onset Alzheimer’s disease; HEPES, 2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid; HRP, horseradish peroxidase; LC-MS/MS, liquid chromatography–tandem mass spectrometry; LOAD, Late-onset Alzheimer’s disease; MBP, myelin basic protein; MCI, Mild cognitive impairment; MRI, Magnetic resonance imaging; MRM, multiple reaction monitoring; PBS, phosphate-buffered saline; PET, positron emission tomography; PFC, pre frontal cortex; PMI, post mortem interval; PSEN1, Presenilin-1; PSEN2, Presenilin-2; PTM, post- translational modification; PVDF, polyvinylidene
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