Deciphering Regulatory Networks in the Mouse Genome

Sethi, Siddharth (2019). Deciphering Regulatory Networks in the Mouse Genome. PhD thesis The Open University.

DOI: https://doi.org/10.21954/ou.ro.00010972

Abstract

Regardless of all the major achievements in the field of genomics and in depth studies of the protein-coding genes, our knowledge about non-coding regions and their contribution in diseases remains incomplete. Large scale projects such as the ENCODE have produced a wealth of sequencing data which can be utilised to study epigenetic features associated with gene regulation. These studies have comprehensively identified regulatory elements such as enhancers in the human genome, but numerous questions still remain on their effect on gene function and disease causation.

The aim of this thesis is to identify enhancer regulatory networks in the mouse genome and investigate their effect on mouse models of human diseases. In order to study enhancer regulation, I have taken two approaches. First, I have produced a catalogue of well-defined multiple enhancer types in a diverse range of mouse tissues and cell-types. By systematically comparing different enhancer types, I found that super- and typical-enhancers have different effect on gene expression, but both are preferentially associated with relevant tissue-type phenotypes. Also genes associated with super- and typical-enhancers exhibit no difference in phenotype effect size or pleiotropy. Second, by utilising publicly available regulatory annotations, my enhancer catalogue and omics data, I have investigated regulatory mechanisms associated with metabolic and circadian mouse models. Here I identified novel regulatory networks or enhancers or transcription factor binding sites pertaining to the mutant mice.

In conclusion, my research has shown the usefulness of integrating enhancer annotations with an array of molecular data and has for the first time shown how different enhancer architectures influence gene function in the mouse genome. This study provides a valuable dataset to further characterise the mechanisms of gene regulation by enhancers in the mouse genome.

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