Discovering molecular mechanisms of human disease through gene sets and networks
Park, Jisoo.
2017
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Abstract: Molecular processes, such as genetic mutations and interactions
between gene products, play a key role in the development of disease. Yet, there is still
not enough known about molecular causes of disease. In this thesis, we introduce methods to
discover hidden connections between biological functions and disease using gene sets and
networks. We first investigate a topological proper... read morety of pathways in protein-protein
interaction networks. We define a new measurement called pathway centrality which measures
the amount of information flow between disease genes and differentially expressed genes
handled by a pathway. We find mediating pathways for three pulmonary diseases (asthma;
bronchopulmonary dysplasia (BPD); and chronic obstructive pulmonary disease (COPD)) using
pathway centrality. Mediating pathways shared by all three pulmonary disorders are mostly
related to inflammation or immune responses and include specific pathways such as cytokine
production, NF Kappa B signaling, and JAK/STAT signaling. We confirm our findings, which
suggest new treatment approaches, both with anecdotal evidence from the literature and via
systematic evaluation using genetic interactions. Second, we identify connections between
developmental processes and disease by statistically testing overlaps between developmental
gene sets and disease gene sets. To handle missing disease-gene association information, we
pool disease genes from specific disease terms to more general disease terms in a disease
taxonomy. Our overlap analysis results for nine developmental gene sets confirms many
expected connections, such as those between cardiovascular disorders and heart development
genes. Closer investigation of our results highlights some unexpected connections, such as
ones between bone development and dementia, heart development and polycystic ovary
syndrome, and lung development and retinopathy of prematurity. These connections have been
further supported by recent publications and again suggest novel therapeutic strategies.
While successful, this work highlights a need for more molecular disease taxonomies to
improve the efficacy of gene pooling. We ran a pilot study to infer disease hierarchies
only using disease-gene association information. We evaluate our inferred disease
hierarchies by comparing to existing ones because there is no gold standard molecular
disease taxonomy available. We find that our inference algorithm is able to recover much of
the structure of existing disease taxonomies. While inference is easier for smaller sets of
disease terms, we found some large disease categories where inference methods perform well,
such as Endocrine System Diseases, Nutritional and Metabolic Diseases, and Respiratory
Tract Diseases. We suspect that the existing hierarchies representing these disease
categories incorporate larger amounts of molecular data, perhaps because they include
several well-studied complex diseases. Overall, we have introduced new computational
methods that highlight novel connections between gene sets and diseases. We expect that our
studies will lead to deeper understanding of underlying mechanisms of human disease, and
ultimately to better support of molecular medicine.
Thesis (Ph.D.)--Tufts University, 2017.
Submitted to the Dept. of Computer Science.
Advisor: Donna Slonim.
Committee: Lenore Cowen, Benjamin Hescott, Kyongbum Lee, and Teresa Przytycka.
Keywords: Computer science, and Bioinformatics.read less - ID:
- qj72pj865
- Component ID:
- tufts:22442
- To Cite:
- DCA Citation Guide EndNote
