MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Doctoral Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Doctoral Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Mechanisms of genetic risk in Alzheimer’s disease

Author(s)
von Maydell, Djuna
Thumbnail
DownloadThesis PDF (101.8Mb)
Advisor
Tsai, Li-Huei
Terms of use
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/
Metadata
Show full item record
Abstract
Sporadic Alzheimer's disease (AD) accounts for the majority of dementia cases worldwide, yet effective treatments remain limited. Genetic variants associated with AD provide insight into disease etiology and highlight potential therapeutic targets. The ε4 allele of the APOE gene is the strongest genetic risk factor for AD, and rare variants in the ABCA7 gene are among the next most significant. Both genes encode lipid transporters, suggesting an important role for lipid metabolism in AD etiology. However, the exact cellular mechanisms through which these variants increase AD risk remain incompletely understood. After a brief introduction in Chapter 1, Chapter 2 demonstrates that damaging ABCA7 variants disrupt neuronal phosphatidylcholine metabolism and mitochondrial function. These defects were reversed by supplementation with the phosphatidylcholine precursor cytidine diphosphate-choline (CDP-choline). Chapter 3 shows that APOE4-expressing oligodendrocytes exhibit altered cholesterol transport and impaired myelination. Pharmacological modulation of cholesterol transport in the brain reversed these defects, improving cognitive function in mouse models. These findings suggest that lipid-related mechanisms represent a class of targetable drivers of AD risk, but it remains unclear whether lipid-targeted treatments would be broadly applicable across AD or restricted to specific disease subtypes. Chapter 4 introduces a practical framework for identifying disease subtypes in high-dimensional biological data based on principles from machine learning and data attribution, and applies it to explore transcriptional subtypes among AD brains. Together, these studies reveal potential mechanisms of genetic risk in AD, highlight lipid disruptions as upstream mediators, and propose a practical framework for uncovering AD subtypes.
Date issued
2025-09
URI
https://hdl.handle.net/1721.1/165147
Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Publisher
Massachusetts Institute of Technology

Collections
  • Doctoral Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.