Measuring Stability of 3D Chromatin Conformations and Identifying Neuron Specific Chromatin Loops Associated with Schizophrenia Risk
Authors
Borrman, Tyler M.Faculty Advisor
Zhiping WengAcademic Program
Bioinformatics and Computational BiologyUMass Chan Affiliations
Bioinformatics and Integrative BiologyDocument Type
Doctoral DissertationPublication Date
2020-11-12Keywords
bioinformatics3D genome
chromatin
TCR-pMHC
protein modeling
Hi-C
Bioinformatics
Computational Biology
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The 23 pairs of chromosomes comprising the human genome are intricately folded within the nucleus of each cell in a manner that promotes efficient gene regulation and cell function. Consequently, active gene rich regions are compartmentally segregated from inactive gene poor regions of the genome. To better understand the mechanisms driving compartmentalization we investigated what would occur if this system was disrupted. By digesting the genome to varying sizes and analyzing the fragmented 3D structure over time, our work revealed essential laws governing nuclear compartmentalization. At a finer resolution within compartments, chromatin forms loop structures capable of regulating gene expression. Genome wide association studies have identified numerous single nucleotide polymorphisms (SNPs) associated with the neuropsychiatric disease schizophrenia. When these SNPs are not located within a gene it is difficult to gain insight into disease pathology; however, in some cases chromatin loops may link these noncoding schizophrenia risk variants to their pathological gene targets. By generating 3D genome maps, we identified and analyzed loops of glial cells, neural progenitor cells, and neurons thereby expanding the set of genes conferring schizophrenia risk. The binding of T-cell receptors (TCRs) to foreign peptides on the surface of diseased cells triggers an immune response against the foreign invader. Utilizing available structural information of the TCR antigen interface, we developed computational methods for successful prediction of TCR-antigen binding. As this binding is a prerequisite for immune response, such improvements in binding prediction could lead to important advancements in the fields of autoimmunity and TCR design for cancer therapeutics.DOI
10.13028/sh3x-v380Permanent Link to this Item
http://hdl.handle.net/20.500.14038/31339Rights
Licensed under a Creative Commons licenseDistribution License
http://creativecommons.org/licenses/by/4.0/ae974a485f413a2113503eed53cd6c53
10.13028/sh3x-v380
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