Revealing the Biological Processes Underlying Neurodegenerative Diseases with Systems Biology Approaches
Name
leventhal_mleventh_phd_computational_and_systems_biology_2025_thesis.pdf
Size
18.94 MB
Format
Adobe PDF
Checksum (MD5)
e1ed588d6c3acffcbd0e808155c02566
Author(s)
Leventhal, Matthew Joseph
Advisor(s)
Fraenkel, Ernest
Date Issued
February 2025
Publisher
Massachusetts Institute of Technology
Abstract
Neurodegenerative diseases are devastating conditions characterized by cognitive decline and increased prevalence with advancing age. Large observational datasets and forward genetic screens from a diversity of disease models advance our understanding of the genetic and molecular risk factors of neurodegenerative diseases. However, the exact genes and pathways that cause neurodegeneration in disease are unclear. In my thesis, I use protein-protein interaction networks to integrate observational molecular data across different disease models and species with genetic screening data to understand the biological processes underlying neurodegeneration in disease. In my first project, I integrate data from human and Drosophila models of Alzheimer’s disease with the results from a screen for age-associated neurodegeneration to characterize biological processes of neurodegeneration in Alzheimer’s disease. I use a protein-protein interaction network to predict how neurodegeneration screen hits and genetic risk factors contribute to Alzheimer’s disease-associated neurodegeneration and DNA damage. My second project uses multiplexed immunofluorescence microscopy in post-mortem Alzheimer’s disease brains to investigate the effects of pathological protein aggregates on the regulators of DNA damage detailed in the first part of my thesis. This work shows significant changes in DNA damage relative to distance from pathological protein aggregates in Alzheimer’s disease brains. My third project presents another example of how I use protein-protein interaction networks to understand neurodegenerative disease genetics. I integrate observational data from human induced pluripotent stem cell-derived cortical neuron models of Huntington’s disease to infer the wild-type behavior of the HTT gene. This work identifies gain-of-function and loss-of-function consequences of CAG repeat expansion in the HTT gene in Huntington’s disease. This thesis presents advances in the applications of protein-protein interaction networks to understand human disease. My work presents advances for understanding how neurodegeneration occurs in Alzheimer’s disease and Huntington’s disease by unifying functional genetic screening data with observational patient data.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Terms of Use
In Copyright - Educational Use Permitted
Copyright retained by author(s)
Persistent DSpace Link
