Integrated computational and experimental analysis of non-neuronal cell molecular mechanisms contributing to Alzheimer's disease progression
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lee-meelim-phd-be-2022-thesis copy.pdf
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Thesis PDF
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14.11 MB
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Author(s)
Lee, Meelim Jasmine
Advisor(s)
Lauffenburger, Douglas A.
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
Alzheimer’s disease (AD) is the most common form of dementia, characterized by symptoms such as memory loss and language disruption. Amyloid beta-containing plaques and hyperphosphorylated tau-containing neurofibrillary tangles (NFTs) are defining characteristics of AD. However, plaques and NFTs are insufficient to robustly predict disease severity, and therapies targeting these disease hallmarks have been largely unsuccessful. Increasingly, there has been focus on non-neuronal cells, mechanisms by which they affect neurotoxicity, and their varied roles in disease progression. In this thesis, we apply systems techniques towards ADrelevant omics datasets to generate new hypotheses regarding non-neuronal cell contributions to disease progression.
First, we conducted cross-species analysis of human and mouse transcriptomics to identify translatable signatures implicating the innate immune system and specifically the Tyro3/Axl/Mer receptor (TAMR) family. AD mouse models are a major component of preclinical evaluation of therapies. However, amyloid and tau dysregulation are often key features, and fidelity of subsequent disease progression to human AD is limited. In this work, we concurrently analyzed human and AD mouse model transcriptomics to identify signatures present in human data and predictive of mouse outcomes. Towards experimental validation of our computational inferences, we evaluated Protein S effects on microglial response to amyloid. Overall, this work provides a computational framework for rational selection of mouse models and presents preliminary data towards our hypothesis that TAMR ligands preferentially binding Mer could modulate absolute and relative TAMR abundance to bias microglia towards a therapeutically beneficial, phagocytic state.
Second, we applied statistical modeling to analyze matched protein-level and AD patient outcome data to identify oligodendrocyte contributions to patient stratification. We focused on histopathological outcomes to identify a tau/oligodendrocyte basis for patient stratification. Subsequently, we modeled distinct stages of disease progression to identify protein-level clusters covarying with cognition more than plaque burden. Clusters negatively associated with cognition were enriched for oligodendrocyte lineage cell peptides, distinct from those underlying our initial tau/oligodendrocyte patient stratification. Together, these supervised analyses highlight oligodendrocyte lineage cell contributions to patient variability and point towards questions of whether this signature is AD specific, generalizable across neurodegenerative diseases, or present prior to the cellular phase of disease progression.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
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