Distal enhancer elements regulate the tissue-specific expression of their target genes via the establishment of physical interactions with the gene promoter. In mice, a cluster of five enhancers, jointly classified as a super-enhancer, specifically upregulate α-globin gene expression during erythroid differentiation. Aside from the Nprl3 gene, whose promoter is located inside this enhancer region, expression-levels of other genes within a short distance (&lt,50kb) of the enhancer region are not affected by the activation of the enhancer in erythroid cells, despite being located within the same sub-TAD in erythroid cells. The CCCTC-binding factor (CTCF) is implicated in the organisation of chromosome topology through the formation of interactions between its binding sites in an orientation-dependent manner. In this thesis, I demonstrate that CTCF functions in vivo as a boundary to maintain α-globin enhancer-promoter specificity in erythroid cells. The study of the local chromatin architecture by next-generation Capture-C reveals that α-globin enhancer and promoter interactions are constrained to a compartment of roughly 70kb. The unidirectional interaction profiles of the α-globin enhancers are delimited by the interactions between two genomic domains flanking the α-globin cluster. Further investigation shows that each of these domains contains several CTCF binding sites orientated in tandem, such that CTCF binding orientation between domains is convergent. Although CTCF binding across the α-globin locus is identical between mouse embryonic stem (ES) cells and erythroid cells, interaction between these domains occurs only in erythroid cells suggesting it is dependent on the formation of tissue-specific α-globin enhancer-promoter interactions. By generating a series of mouse models, deleting CTCF binding sites at the α-globin enhancers singly and in combination, I show that the deletion of two CTCF binding sites directly flanking the enhancer cluster results in a shift in interactions between flanking domains, away from the enhancer region. This leads to an expansion of enhancer interactions to include two genes directly upstream of the α-globin enhancers: Rhbdf1 and Mpg. Despite the Rhbdf1 gene being subject to polycomb group protein-mediated gene repression in erythroid cells, ablation of CTCF binding results in increased interactions between both the Rhbdf1 and Mpg gene promoters and the α-globin enhancers and concurrent strong transcriptional upregulation of both genes. The Rhbdf1 gene promoter acquires the active histone mark H3K4me3, but doesn't lose Polycomb Repressive Complex 2 (PRC2) mark H3K27me3 or binding of its catalytic component Ezh2. Despite the presence of this repressive mark, robust levels of Rhbdf1 expression are detected at levels higher than those in ES cells where this gene is actively expressed under the influence of its own enhancer. I conclude that regulation of the direction of enhancer interactions by CTCF is required for the promoter specificity of enhancers and the maintenance of transcriptional states of nearby genes.

Transcription factor (TF) binding has been studied extensively in relation to binding site affinity and chromosome modifications; however, the relationship between genome spatial organisation and transcription factor binding is not well studied. Using the recently available high resolution Hi-C contact map of human GM12878 lymphoblastoid cells, we investigated computationally the genome-wide spatial co-localization of transcription factor binding sites, for both within the same type and between different types. First, we observed a strong positive correlation between site occupancy and homotypic TF co-localization based on Hi-C contacts, consistent with our predictions from biophysical simulations of TF target search. This trend is more prominent in binding sites with weak binding sequences and within enhancers, suggesting genome spatial organisation plays an essential role in determining binding site occupancy, especially for weak regulatory elements. Furthermore, when investigating spatial co-localization between different TFs, we discovered two distinct co-localization networks of TFs in lymphoblastoid cells, one of which is enriched in lymphocyte specific pathways and distal enhancer binding. These two TF networks have strong biases for either the A1 or A2 chromosome subcompartment, but nonetheless are still preserved within each, indicating a potential causal link between cell-type-specific transcription factor binding and chromosome subcompartment segregation. We called 40 pairs of significantly co-localized TFs according to the genome wide Hi-C contact map, which are enriched in previously reported, physical interactions, thus linking TF spatial network to co-functioning. In addition to the above main project, I also worked on a side project to find compute-efficient ways in scaling binding site strength across different TFs based on Position-Weight-Matrices (PWM). While common bioinformatics tools produce scores that can reflect the binding strength between a specific TF and the DNA, these scores are not directly comparable between different TFs. We provided two approaches in estimating a scaling parameter $\lambda$ to the PWM score for different TFs. The first approach uses a PWM and background genomic sequence as input to estimate $\lambda$ for a specific TF, which we applied to show that $\lambda$ distributions for different TF families correspond with their DNA binding properties. Our second method can reliably convert $\lambda$ between different PWMs of the same TF, which allows us to directly compare PWMs that were generated by different approaches.

Within every human cell, approximately two meters of DNA must be compacted into a nucleus with a diameter of around ten micrometers. Alongside this daunting storage problem, the 3D organisation of the genome also helps determine which genes are up- or down-regulated, which in turn effects the functionality of the cell itself. While the organisational structure of the genome can be revealed using experimental techniques such as chromosome conformation capture and its high-throughput variant Hi-C, the mechanisms driving this organisation are still unclear. The first two results chapters of this thesis use molecular dynamics simulations to investigate the effect of a potential organisational mechanisms for DNA known as the "bridging-induced attraction". This mechanism involves multivalent DNA-binding proteins bridging genomically distant regions of DNA, which in turn promotes further binding of proteins and compaction of the DNA. In chapter 2 (the first results chapter) we look at a model where proteins can bind non-specifically to DNA, leading to cluster formation for suitable protein-DNA interaction strengths. We also show the effects of protein concentration on the DNA, with a collapse from a swollen to a globular phase observed for suitably high protein concentrations. Chapter 3 develops this model further, using genomic data from the ENCODE project to simulate the "specific binding" of proteins to either active (euchromatin) or inactive (heterochromatin) regions. We were then able to compare contact maps for specific simulated chromosomes with the experimental Hi-C data, with our model reproducing well the topologically associated domains (TADs) seen in Hi-C contact maps. In chapter 4 of the thesis we use numerical methods to study a model for the coupling between DNA topology (in particular, supercoiling in DNA and chromatin) and transcription in a genome. We present details of this model, where supercoiling flux is induced by gene transcription, and can diffuse along the DNA. The probability of transcription is also related to supercoiling, as regions of DNA which are negatively supercoiled have a greater likelihood of being transcribed. By changing the magnitude of supercoiling flux, we see a transition between a regime where transcription is random and a regime where transcription is highly correlated. We also find that divergent gene pairs show increased transcriptional activity, along with transcriptional waves and bursts in the highly correlated regime { all these features are associated with genomes of living organisms.

Previously studies used correlation of DNase I hypersensitivity sites sequencing (DNase-seq) experiments to predict interactions between enhancers and its target promoter gene. We investigate the correlation methods Pearson’s correlation and Mutual Information, using DNase-seq data for 100 cell-types in regions on chromosome one. To assess the performances, we compared our results of correlation scores to Hi-C data from Jin et al. 2013. We showed that the performances are low when comparing it to the Hi-C data, and there is a need of improved correlation metrics. We also demonstrate that the use of Hi-C data as a gold standard is limited, because of its low resolution, and we suggest using another gold standard in further studies.

Chromatin is a complex polymer molecule in eukaryotic cells, primarily consisting of DNA and histones. Many works have shown that the 3D folding of chromatin structure plays an important role in DNA expression. The recently proposed Chro- mosome Conformation Capture technologies, especially the Hi-C assays, provide us an opportunity to study how the 3D structures of the chromatin are organized. Based on the data from Hi-C experiments, many chromatin 3D structure modeling methods have been proposed. However, there is limited ground truth to validate these methods and no robust chromatin structure alignment algorithms to evaluate the performance of these methods. In our work, we first made a thorough literature review of 25 publicly available population Hi-C-based chromatin 3D structure modeling methods. Furthermore, to evaluate and to compare the performance of these methods, we proposed a novel data simulation method, which combined the population Hi-C data and single-cell Hi-C data without ad hoc parameters. Also, we designed a global and a local alignment algorithms to measure the similarity between the templates and the chromatin struc- tures predicted by different modeling methods. Finally, the results from large-scale comparative tests indicated that our alignment algorithms significantly outperform the algorithms in literature.

The corpus of reference genomes is rapidly expanding as more and more genome assemblies are released for a wide variety of species. The constant progress in sequencing technologies has led to the release in 2021 of a first complete, telomere-to-telomere, gap-less assembly of a human genome, yet a myriad of eukaryote species still lack genomic resources. For animals, genomic projects have focused on species closely related to humans (vertebrates) and those with an impact on health and agriculture. By contrast, there is still a dearth of non-vertebrate genomes that poorly represents their tremendous diversity (about 95% of animal diversity).Haploid chromosome-level genome assemblies using long reads and chromosome conformation capture (such as Hi-C) have become a standard in recent publications. To provide a haploid representation of diploid and polyploid genomes, assemblers collapse haplotypes into a single sequence, yet they are sensitive to high levels of heterozygosity and often yield fragmented assemblies with artefactual duplications. I tackled these shortcomings with two strategies: improving collapsed assemblies with a comprehensive long-read assembly methodology tuned for highly heterozygous genomes; and separating haplotypes to obtain phased assemblies using long reads and Hi-C. The assemblies were finally brought to chromosome-level scaffolds with a new Hi-C scaffolder, which demonstrated its efficiency on genomes of non-model organisms.These methods were applied to generate chromosome-level assemblies of three species for which none or few assemblies of closely related species were available: the bdelloid rotifer Adineta vaga, the coral Astrangia poculata, and the chaetognath Flaccisagitta enflata. These high-quality assemblies contribute to filling the current gaps in non-vertebrate genomics and pave the way for future sequencing initiatives aiming to generate such reference assemblies for all the species on Earth. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Understanding 3D genome structure is crucial to learn how chromatin folds and how genes are regulated through the spatial organization of regulatory elements. Various technologies have been developed to investigate genome architecture. These technologies include ligation-based 3C Methodologies such as Hi-C and Micro-C, ligation-based pull-down methods like Proximity Ligation-Assisted ChIP-seq (PLAC Seq) and Paired-end tag sequencing (ChIA PET), and ligation-free methods like Split-Pool Recognition of Interactions by Tag Extension (SPRITE) and Genome Architecture Mapping (GAM). Although these technologies have provided great insight into chromatin organization, a systematic evaluation of these technologies is lacking. Among these technologies, Hi-C has been one of the most widely used methods to map genome-wide chromatin interactions for over a decade. To understand how the choice of experimental parameters determines the ability to detect and quantify the features of chromosome folding, we have first systematically evaluated two critical parameters in the Hi-C protocol: cross-linking and digestion of chromatin. We found that different protocols capture distinct 3D genome features with different efficiencies depending on the cell type (Chapter 2). Use of the updated Hi-C protocol with new parameters, which we call Hi-C 3.0, was subsequently evaluated and found to provide the best loop detection compared to all previous Hi-C protocols as well as better compartment quantification compared to Micro-C (Chapter 3). Finally, to understand how the aforementioned technologies (Hi-C, Micro-C, PLAC-Seq, ChIA-PET, SPRITE, GAM) that measure 3D organization could provide a comprehensive understanding of the genome structure, we have performed a comparison of these technologies. We found that each of these methods captures different aspects of the chromatin folding (Chapter 4). Collectively, these studies suggest that improving the 3D methodologies and integrative analyses of these methods will reveal unprecedented details of the genome structure and function.

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.

Promoter-enhancer interactions are essential for gene regulating, Capture Hi-C is a chromosome conformation capture method to map promoter-enhancer interactions at high resolution. We have Capture Hi-C data forGM12878 cells, immortalized primary B lymphocytes, in three replicates. Although Capture Hi-C maps enhancer elements together with the promoters they regulate, the overlap between enhancer datasets produced by other methods such as ChIP-seq and Capture Hi-C is lower than expected. In order to understand the reasons for lower overlap, we investigated the enhancer potential of replicated and non-replicated Capture Hi-C interactors, as well as enhancer overlapping and non-overlapping Capture Hi-C interactors. We performed a systematic comparison between our interactor and experimental regulatory and transcriptomic datasets to determine the extent of enhancer mapping. The results show replicated interactors have higher enhancer potential than non-replicated ones. However, there is evidence that interactors not overlapping with experimental validated regulatory datasets can also potentially be true enhancers.

本研究探討高通量染色體捕捉技術 (high-throughput chromosome conformation capture, Hi-C) 實驗所產生的關聯矩陣資料之正規化方法。已知該類實驗主要用來測量染色體之間的空間距離，正規化的目的是移除資料中的系統性偏差，本文主要針對基因特徵所造成之偏差。有別於Hu等人 (2012) 所提出的「局部基因特徵正規化法」(local genome feature normalization, LGF法)，我們所提出的「二次函數正規化法」(quadratic function normalization, QF法) 建立在更為一般化的二次對數模型與負二項分配假設上。本研究透過模擬實驗以及人類淋巴細胞資料 (GSE18199) 來評估QF法的表現，並且與其他方法比較。在模擬實驗中，我們發現當模型正確時，QF法能有效消除偏差。在實例中，當基因特徵偏差被消除後，則染色體之間的相對距離在重複實驗資料之間有更為一致的結果。另一方面，我們發現實驗所採用的限制酶影響關聯矩陣的結果，而且運用這些正規化方法並不能有效消除限制酶造成的偏差。 / Recently, the high-throughput chromosome conformation capture (Hi-C) experiment is developed to explore the three-dimensional structure of genomics. To assess the chromosomal interaction, a contact matrix is produced from a Hi-C experiment. Very often, systematic technical biases appear in the contact matrix and lead to inadequate conclusions. Consequently, data normalization to remove these biases is essential and necessary prior advanced inference. In this research, we propose the so-called quadratic function normalization method, which is a modification of the local genome feature normalization (Hu et al., 2012) by considering a more general model. Simulation studies are conducted to evaluate the proposed method. When the model assumption holds, the proposed method has adequate performance. Further, a Hi-C data set of a human lymphoblastoid cell GSE18199 is employed for a comparison of our method and two existing methods. It’s observed that normalization improves the reproducibility between experimental replicates. However, the effect of normalization is lean in eliminating the bias of restriction enzymes.