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A computational multiomics method for quantifying activity and regulatory mode of transcription factors and its application in leukemia

Berest, Ivan

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Recent breakthroughs in sequencing technologies allowed researchers to generate extensive amounts of data characterizing cellular regulation at many levels. Consequently, this boosted our understanding of gene regulatory networks responsible for different biological processes and highlighted the overall importance of transcription factors (TFs). TFs are dynamic mediators that react to both intra- and extracellular changes in order to ultimately transmit signals and execute genetically inherited gene regulatory programs in a time- and location-specific manner. However, it is still challenging to quantify in vivo TF specific binding occupancy and dynamics due to the high complexity of the regulatory part of the genome. Modern technologies measuring chromatin changes (e.g., chromatin accessibility, DNA methylation, histone modifications) can now generate testable hypotheses about the effects of TF binding on gene regulation. In this thesis, I mainly describe the novel computational tool diffTF, a multiomics data integration tool for globally assessing differential TF activity and classifying TFs into transcriptional activators and repressors (by integrating chromatin accessibility and gene expression data). We applied it to a recently published ATAC-seq dataset from a cohort of chronic lymphocytic leukemia (CLL) patients and identified dozens of differential active TFs representing two different CLL subtypes that are inherently linked to tumour progression. In addition, we integrated gene expression data from corresponding RNA-seq and were able to globally predict an activating or repressive role for 40% of the expressed TFs. We validated the approach on an independent CLL dataset and showed that the majority of TFs does not change their mode of action upon genetic or environmental perturbations. Finally, we extensively tested and benchmarked diffTF to validate its technical robustness. We also applied diffTF to a multiomics dataset from the mouse hematopoietic differentiation system and targeted potential TFs that are disturbed upon epigenetic dysregulation driven by a Tet methylcytosine dioxygenase 2 (TET2) knockout in acute myeloid leukemia (AML). TET2 plays an essential role in the cellular DNA methylation balance and is known to be frequently mutated in leukemia. We used the first high-quality TET2 binding map to identify TF families that can facilitate TET2 binding in the genome. In summary, we developed a novel hypothesis-generation computational tool that can, in a data-driven way, identify key regulators of cellular biological processes based on chromatin and expression data.

Item Type: Dissertation
Supervisor: Gavin, Prof. Dr. Anne-Claude
Place of Publication: Heidelberg
Date of thesis defense: 13 July 2020
Date Deposited: 15 Sep 2020 10:02
Date: 2020
Faculties / Institutes: The Faculty of Bio Sciences > Dean's Office of the Faculty of Bio Sciences
Subjects: 500 Natural sciences and mathematics
570 Life sciences
Controlled Keywords: Multiomics, Transcription factors, Chromatin accessibility
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