Decoding Transcription Regulatory Patterns in Health and Disease
Transcriptional control of gene expression is known to be a cell-type specific process that depends on many different factors: including the underlying DNA sequence, combinatorial patterns of transcription factors (TFs) and their associated motifs, chromatin accessibility, and DNA-DNA looping contac...
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| Format: | Dissertation |
| Language: | English |
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ProQuest Dissertations & Theses
01-01-2021
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| Online Access: | Get full text |
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| Summary: | Transcriptional control of gene expression is known to be a cell-type specific process that depends on many different factors: including the underlying DNA sequence, combinatorial patterns of transcription factors (TFs) and their associated motifs, chromatin accessibility, and DNA-DNA looping contacts. However, modeling the relationship between DNA and its regulatory elements while learning how these regulatory complexes lead to cell-type specific function within a 3-dimensional context remains difficult. Additionally, sequence variants within DNA are known to modulate risk for many polygenic diseases--with oncogenic, neuropsychiatric, and inflammatory etiologies, among others. As these variants are mostly in non-coding regions of the genome, assessing the functional impact of variants within the cell and how they might contribute to disease pathogenesis remains a computational and experimental challenge.In this dissertation, I present data representation and modeling strategies to understand cell-type specific transcription regulatory mechanisms in healthy cells and analyze how these mechanisms are disrupted in complex polygenic diseases. First, I use an epigenomic and transcriptomic resource of 15 different epithelial cell types to extract features of transcription regulation from the dataset in an unbiased manner and to build a data representation of transcription regulation that accurately predicts cell type. I then show that different patterns of transcription factor motif cooperativity exist in healthy cells and that this synergistic behavior is altered in the context of cancer. Next, as part of a collaborative effort, I generate a compendium of non-coding single nucleotide variants (SNVs) for neuropsychiatric disorders that alter transcription-regulatory activity in a developing neural cell system. The analytical framework built from this dataset links regulatory variants to disease-relevant genes, pathomechanisms, therapeutics, and clinical manifestations of disease using expression-gene mapping, network analyses, chromatin looping, and statistical enrichment methods. Additionally, I develop a network-based approach to identify genes relevant for psychiatric disease diagnosis and to prioritize treatment options. Finally, I add a perspective on the strengths and limitations of modern network methods--including network building strategies, machine learning techniques, and inference applications--for biological applications. In summary, I hope that these resources and methods--which provide further insight into transcription regulation--have translatable uses in the clinical setting for complex disease prognosis, diagnosis, and therapy selection. |
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| ISBN: | 9798374422955 |