There are approximately 30 trillion cells in the human body, and nearly every cell has the same genomic sequence. Yet, due to differential gene expression, we have around 200 distinct cell types each with varying functionalities. The cell type specific states are maintained via the binding of multiple regulatory proteins to different locations along the genome in a process known as transcriptional regulation. Additionally, disruptions to the transcriptional regulation process may lead to the development of disease. Hence, uncovering the complex interplay of protein-DNA interactions along the genome is of critical importance. The advent of technologies probing the genomic sequence, as well as the development of powerful computational modeling techniques to relate DNA sequences to molecular phenotype, has enabled the understanding of many molecular processes genome wide. However, these computational methods require significant adaptation to biological systems - to accurately and fully account for the biology behind the molecular processes, as well as the biases associated with the data generating systems and processes. In this thesis, we address three main issues that arise from the use of omics data, more specifically ChIP-seq data, when identifying regulatory proteins along the genome. The first part of the thesis involves the study of the biases and noise associated with ChIP-seq experiments. Each experiment is prone to noise and bias, and as such we propose the use of a customized set of weighted controls, instead of equally weighted controls, for each ChIP-seq experiment in the peak calling process to mitigate the noise and bias. To do this, we implement a peak calling algorithm, called Weighted Analysis of ChIP-seq (WACS), which is an extension of the well-known peak caller MACS2, to incorporate the weighted controls in the peak calling process. We show that our approach assists in a better approximation of the noise distribution in controls, and fundamentally improves our understanding of ChIP-seq signals and their biases. Another aspect we explore in this thesis is the ability to uncover cell type specificity of transcription factor binding from the ChIP-seq data. A transcription factor may bind to various parts of the genome in different cell types, due to modifications in the DNA-binding preferences of the transcription factor, or other mechanisms, such as chromatin accessibility or cooperative binding, thus leading to a "DNA signature" of differential binding. We develop a deep learning approach, called SigTFB (Signatures of TF Binding) and conduct a wide scale analysis of hundreds of transcription factors to identify and quantify the varying degrees of cell type specific DNA signatures of various transcription factors across cell types. We also assess the consistency of cell type specificity for a specific transcription factor when assayed by different antibodies. We show that many transcription factors are indeed cell type specific, while others are more general with lower cell type specificity. Finally, to further explain the biology behind a transcription factor's cell type specificity, or lack that of, we conduct a wide scale motif enrichment analysis of all transcription factors in question. We show that cell type specific transcription factors are typically associated with corresponding differences in motif enrichment and gene expression. Together, these contributions deepen our knowledge of transcription factor binding, and how experimental and cell type specific variations can be uncovered.

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A functional sense of smell is mediated by Olfactory Receptor proteins (ORs), which reside in olfactory sensory neurons (OSNs) in the epithelium of our nose. Only a singular OR allele out of roughly 2,400 other OR alleles is expressed in every OSN⁽¹˒ ²⁾. Singular expression of the active OR gene occurs in a unique transcriptional hub⁽³⁻⁵⁾. This hub contains one OR promoter and many interchromosomal enhancers that converge upon the hub from far nuclear distances⁽⁵˒ ⁶⁾. Once in the hub, the enhancers work in tandem with each other and with the transcription factors (TFs) Lhx2, EBF, and their cofactor, Ldb1⁽⁵˒ ⁷˒ ⁸⁾ The Greek islands contain a novel “composite” motif containing an Lhx2 and EBF binding site directly next to each other⁽⁸⁾. My work aims to understand how these proteins interact with each other and the composite motif to contribute to the accumulation of many enhancers around only a single promoter in the hub. Our findings illuminate how transcription factor interactions contribute to the hub's unique DNA architecture. To investigate the biochemical foundation of OR hubs, we used 𝑒. 𝑐𝑜𝑙𝑖 to grow and purify full-length and truncated forms of Lhx2, Ebf1, and Ldb1. We used the recombinant proteins with other biochemical methods to characterize the interactions between Lhx2, Ebf1, Ldb1, and different types of DNA found in the OR hub. We used EMSAs to quantify the binding affinity that Lhx2 and Ebf1 have for promoter versus enhancer DNA. Finally, we mixed the purified full-length proteins and used fluorescence microscopy to visualize their behavior in solution. This research combined with in vivo imaging in the Lomvardas lab provides a better understanding as to how molecular interactions 𝑖𝑛 𝑣𝑖𝑡𝑟𝑜 contribute to the hub’s architecture 𝑖𝑛 𝑣𝑖𝑣𝑜, and ultimately, stable OR expression. Our biochemical studies suggest that Lhx2 and Ebf1 can bind at the same time to a single composite motif yet they seem to bind independently of one another. We have used EMSAs to determine that Lhx2 binds much better to OR enhancer DNA compared to Ebf1 and that it might stabilize enhancer contacts. We have also found that Lhx2 and Ebf1 do not cooperatively bind to enhancers- indicating that affinity alone does not explain the accumulation of TFs to the OR hub. Our 𝑖𝑛 𝑣𝑖𝑡𝑟𝑜 imaging shows that Lhx2, Ebf1, and Ldb1 self-assemble into rigid nucleoprotein condensates driven by the composite motif of enhancer DNA. This imaging work also reveals that Lhx2 and Ldb1 are scaffolding proteins with low mobility which drive rigid condensate formation over enhancers. Ebf1 displays more plasticity and turnover into condensates indicating that it plays a more complex role as a recruited factor to these assemblies. We have coupled this data with 𝑖𝑛 𝑣𝑖𝑣𝑜 imaging of endogenous Lhx2, Ebf1, and Ldb1 to find that these factors display similar binding and dynamics 𝑖𝑛 𝑣𝑖𝑣𝑜. This data helps to provide a biophysical model of how OR hubs support multi-enhancer and protein-rich environments that are succinct from their surrounding environment. Our studies suggest that the OR hub forms a rigid phase separated compartment in the nucleus- driven by Lhx2 and Ldb1. This solid-like phase separation may contribute to how singular OR expression is achieved. Such long-range enhancer contacts must stay assembled long-term for continuous OR transcription. Therefore, traditional TF DNA binding dynamics would not explain the longevity of these contacts in the OR hub. This work challenges the traditional model of liquid phase separated nuclear compartments and may provide a broader understanding to how long range and inter-chromosomal compartments are maintained.

京都大学 / 新制・課程博士 / 博士(農学) / 甲第25357号 / 農博第2623号 / 新制||農||1109(附属図書館) / 京都大学大学院農学研究科食品生物科学専攻 / (主査)教授 保川 清, 教授 井上 和生, 教授 谷 史人 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

For self-renewal, embryonic stem cells (ESCs) require the expression of specific transcription factors accompanied by a particular chromosome organization to maintain a balance between pluripotency and the capacity for rapid differentiation. However, how transcriptional regulation is linked to chromosome organization in ESCs is not well understood. Here we show that the cohesin component RAD21 exhibits a functional role in maintaining ESC identity through association with the pluripotency transcriptional network. ChIP-seq analyses of RAD21 reveal an ESC specific cohesin binding pattern that is characterized by CTCF independent co-localization of cohesin with pluripotency related transcription factors Oct4, Nanog, Sox2, Esrrb and Klf4. Upon ESC differentiation, most of these binding sites disappear and instead new CTCF independent RAD21 binding sites emerge, which are enriched for binding sites of transcription factors implicated in early differentiation. Furthermore, knock-down of RAD21 causes expression changes that are similar to expression changes after Nanog depletion, demonstrating the functional relevance of the RAD21 - pluripotency transcriptional network association. Finally, we show that Nanog physically interacts with the cohesin or cohesin interacting proteins STAG1 and WAPL further substantiating this association. Based on these findings we propose that a dynamic placement of cohesin by pluripotency transcription factors contributes to a chromosome organization supporting the ESC expression program.

Herewithin, two transcription factor (TF) regulatory complexes were investigated. A bHLH–MYB–WDR (BMW) DNA-binding complex from maize was the first complex to be studied. R, a maize bHLH involved in the activation of genes in the anthocyanin pathway, had been characterized to indirectly bind DNA despite the presence of a functional DNA-binding domain. Findings presented here reveal that this is only partially correct. Direct DNA-binding by R was found to be dependent upon two distinct dimerization domains that function as a switch. This switch-like mechanism allows R to be repurposed for the activation of promoters of differing cis-element structure. The second regulatory complex studied was of the Arabidopsis thaliana MIKC-MADS TF family. For many TFs, DNA-binding site recognition is relatively straightforward and very sequence specific, while others exhibit relaxed sequence specificity. MADS-domain TFs are one family of TFs with a wider range of cis-element sequences. Though consensus cis-element sequences have been determined for various MADS-domains, correctly predicting and identifying biologically functional cis-elements has been a challenge. In order to study the influence of nucleobase associations within the cis-element, a DNA-Protein Interaction (DPI)-ELISA method was modified and optimized to screen a panel of specific probes. Screening of the SEP3 homodimer against a panel of sequential, palindromic probes revealed that nucleobases in position -1:+1 of the CArG-box influence binding strength between the MADS-domain and DNA. Additionally, the specificity of AGL15 towards CT-W6-AG forms was discovered to be determined by the functional groups present in the minor groove at position -4:+4 using inosine:cytosine (I:C) base pairs. Finally, the FLC–SVP MADS-domain heterodimer, bound to a native cis-element, was modeled and binding simulated using molecular dynamics. In conjunction with simulations of AGL15 and SEP3 homodimers, a potential binding mechanism was identified for this unique heterodimer. DNA sequence recognition by the MADS-domain was found to occur asymmetrically. In the case of the FLC–SVP heterodimer, the direction of asymmetrical DNA-binding in heterodimers was found to be fixed. Furthermore, the molecular dynamics simulations provided insight towards understanding the results generated from previous DPI-ELISA experiments, which should provide an improved means for predicting biologically significant CArG-boxes around genes.

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Malgré les progrès des traitements des cancers du sein, ceux-ci demeurent la seconde cause de mortalité par cancer au Canada. Parmi les gènes associés aux cancers du sein, le récepteur des œstrogènes ERα est exprimé dans plus de 70% des tumeurs mammaires, qui prolifèrent en réponse aux œstrogènes, faisant de lui une cible de choix. ERα est un facteur de transcription ligand-dépendant, liant des éléments de réponse PuGGTCAnnnTGACCPy. Afin d’examiner la capacité des récepteurs nucléaires à reconnaitre de nouveaux motifs ADN, des mutants aux capacités de liaison modifiées ont été générés. Parmi les quatre résidus interagissant avec l’ADN, R211 ne peut pas être modifiée sans perdre complètement la liaison du récepteur à l’ADN. Néanmoins, les mutations combinées de plusieurs acides aminés contactant les bases de l’ERE ont généré des récepteurs capables de reconnaitre de nouveaux motifs, tout en conservant des niveaux de transactivation efficaces. L’utilisation potentielle des récepteurs nucléaires comme outils de thérapie génique hormono-dépendant, repose sur la prédiction des motifs de liaison efficaces. Étant donné son importance dans la carcinogenèse mammaire, ERα est une cible cruciale des thérapies anti-néoplastiques. L’anti-œstrogène total, ICI, induit la dégradation de ERα et l’arrêt de la croissance des cellules tumorales mammaires ERα-positives. De plus, la nouvelle drogue anti-tumorale HDACi, SAHA, module la voie de signalisation des œstrogènes et possède des propriétés prometteuses en association avec d’autres traitements anti-tumoraux. En effet, le co-traitement ICI et SAHA a un impact synergique sur l’inhibition de la prolifération des cellules mammaires tumorales ERα-positives. Cette synergie repose sur la coopération des effets de ICI et SAHA pour réduire les niveaux protéiques de ERα et bloquer la progression du cycle cellulaire via la modulation de la transcription des gènes cibles des œstrogènes. En fait, les fortes doses de HDACis masquent rapidement et complètement la signalisation transcriptionnelle des œstrogènes. De plus, les gènes cibles primaires des œstrogènes, contenant des EREs, présentent la même régulation transcriptionnelle en réponse aux fortes doses de SAHA ou du co-traitement, avec des doses utilisables en clinique de ICI et SAHA. En fait, ICI mime l’impact des fortes doses de SAHA, en dégradant ERα, potentialisant ainsi la répression de la transcription ERE-dépendante par SAHA. Finalement, la synergie des effets de ICI et SAHA pourrait augmenter l’efficacité des traitements des tumeurs mammaires. / Despite the progress in breast cancer therapy, it is still the second cause of death by cancer in the western world. The estrogen receptor alpha (ER) is expressed in more than 70% of breast tumors, which then proliferate in response to E2 stimulation. ERα is a ligand-dependant transcription factor, which binds to palindromic response elements composed of PuGGTCA motifs. In order to examine the capacity of nuclear receptors to be tailored to recognize novel DNA motifs, rational design of ERα mutants with altered binding specificity were achieved. Of the four amino-acids interacting with DNA, R211 cannot be altered to another residu without a complete loss of DNA binding. However, combined mutagenesis of the amino-acids contacting the bases of the ERE generate receptors that recognized a new type of motifs with efficient transcriptional transactivation levels. In order to consider nuclear receptors as potential hormonal-dependent tools for genetic therapy, bio-informatic models predicting DNA binding are needed. Considering its importance in breast carcinogenesis, ERα is a crucial target for anti-neoplastic therapies. The full anti-estrogen ICI provokes ER degradation and ER-positive breast cancer growth arrest. Moreover, the new HDACi anti-tumoral drug, SAHA modulates the estrogen pathway and have promising properties in association with other anti-neoplastic drugs. In this context, our goal was to determine the effects of the ICI and SAHA combined treatment on breast cancer cell proliferation. The combined treatment has a synergistic impact on inhibiting ERα-positive breast cancer cell growth. It occurs through the cooperation of ICI and SAHA to reduce ER protein level, and to block the cell cycle progression through the transcriptional regulation of E2-target genes. Actually, high doses of HDACis completely and rapidly mask E2-transcriptional signaling. Moreover, primary E2-target genes show the same transcriptional regulation in presence of high doses of SAHA as with the co-treatment, using clinically feasible doses of ICI and SAHA. In fact, ICI mimics the impact of high doses of SAHA through ER degradation, and then potentiate the repressional effect of low doses of SAHA on ERE-dependant transcription. Finally, the synergy of ICI and SAHA might increase the potency of breast cancer therapy.