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Multi-modal assessment of impact of mutations on the genome

Author
Das, Maitreya
Published
[University Park, Pennsylvania] : Pennsylvania State University, 2023.
Physical Description
1 electronic document
Additional Creators
Girirajan, Santhosh
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Restricted (PSU Only).
Summary
The human genome comprises of functional genes and non-coding sequences. These non-coding regions further consist of short stretches of DNA that regulate gene expression called cis-regulatory elements (CREs). The sequences typically mediate gene regulation in a spatio-temporal manner through binding of various transcription factors (TF), co-factors and RNA polymerase II. Transcriptional control elements of genes referred to as 'enhancers' are a particularly dynamic and ambiguous type of CRE that have the ability to regulate gene expression proximal or distal, upstream or downstream of multiple target genes as well as work together with other enhancers and therefore form potential connecting links between multiple gene networks and regulatory pathways. Hence, due to their extremely dynamic behavior, even over four decades of research has not been able to completely unravel their regulatory mechanisms, besides a basic set of functional principles. In the event of a genomic mutation, multiple aspects of the cell may be impacted including enhancers. This is particularly significant since impaired enhancer activity can affect multiple target genes, pathways, networks and disturb general cellular homeostasis. However, the majority of current mutational assessments are typically limited to differential gene expression, chromatin assembly investigations and phenotypic assessments, thereby largely overlooking the effect of the mutations on enhancer activities. In this dissertation, we investigate various aspects of mutational consequences on the genome with a focus on enhancers. In particular, we demonstrate a novel strategy to quantify the direct impact of mutations on enhancer activity. Using an adaptation of a massively parallel reporter assay called STARR-seq, we quantify the effect of deletion of six TFs, each with a diverse set of functions on over 250,000 candidate enhancer fragments. We classify these enhancers into seven distinct categories based on their response to the mutation and uncover new insights on TF-enhancer dynamics, effect of enhancer on target gene expression and enhancer logic. For the latter, we trained linear and deep learning algorithms to discriminate between the different categories of enhancers observed and successfully predicted their activity and response to each mutation. Finally, we apply this approach to testing of the effect of a neurodevelopmental disorder associated CNV at the 16p12.1 chromosomal region We report new insights into affected enhancers and their target genes, thereby revealing an additional layer of information required to accurately connect genomic effects to downstream phenotypes. Thus, we highlight the crucial importance of understanding enhancer biology and enhancer mediated gene regulation, especially in the context of genomic mutations. We further acknowledge the extreme challenges associated with assaying enhancers and emphasize on the need for more integrative, collaborative, and reproducible efforts to deconvolute the mysteries of the non-coding genome.
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Dissertation Note
Ph.D. Pennsylvania State University 2023.
Technical Details
The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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