Date Degree Awarded

Fall 12-15-2017

Degree Type

Restricted to Claremont Colleges Dissertation

Degree Name

PHD in Applied Life Sciences

First Thesis/Dissertation Advisor

Animesh Ray

Second Thesis/Dissertation Advisor

Ian Phillips

Third Thesis/Dissertation Advisor

Seongjoon Koo

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Abstract

Huntington’s disease (HD) is a debilitating neurodegenerative disorder with a complex pathophysiology. Despite extensive studies to study the disease, the sequence of events through which mutant Huntingtin (mHtt) protein executes its action still remains elusive. The phenotype of HD is an outcome of numerous processes initiated by the mHtt protein along with other proteins that act as either suppressors or enhancers of the effects of mHtt protein and PolyQ aggregates. Utilizing an integrative systems biology approach, I construct and analyze a Huntington’s disease integrome using human orthologs of protein interactors of wild type and mHtt protein. Analysis of this integrome using unsupervised machine learning methods reveals a novel connection linking mHtt protein with chromosome condensation and DNA repair. I generate a list of candidate genes that upon validation in a yeast and drosophila model of HD are shown to affect the mHtt phenotype and provide an in-vivo evidence of our hypothesis. A separate supervised machine learning approach is applied to build a classifier model that predicts protein interactors of wild type and mHtt protein. Both the machine learning models that I employ, have important applications for Huntington’s disease in predicting both protein and genetic interactions of huntingtin protein and can be easily extended to other PolyQ and neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease.

Rights Information

Copyright 2017 Sonali J Lokhande

DOI

10.5642/kgitd/4

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