Heterogeneity and Intrinsic Variation in Spatial Genome Organization [preprint]

The genome is hierarchically organized in 3D space and its architecture is altered in differentiation, development and disease. Some of the general principles that determine global 3D genome organization have been established. However, the extent and nature of cell-to-cell and cell-intrinsic variability in genome architecture are poorly characterized. Here, we systematically probe the heterogeneity in genome organization in human fibroblasts by combining high-resolution Hi-C datasets and high-throughput genome imaging. Optical mapping of several hundred genome interaction pairs at the single cell level demonstrates low steady-state frequencies of colocalization in the population and independent behavior of individual alleles in single nuclei. Association frequencies are determined by genomic distance, higher-order chromatin architecture and chromatin environment. These observations reveal extensive variability and heterogeneity in genome organization at the level of single cells and alleles and they demonstrate the coexistence of a broad spectrum of chromatin and genome conformations in a cell population

The Role of Epidermal Enhancer 923 in the Chromatin Architecture and Transcriptional Regulation of the Epidermal Differentiation Complex

The epidermis covers the surface of the skin and provides a functional barrier across the entire body. Epidermal cells or keratinocytes proliferate in the innermost basal layer and migrate upwards into the suprabasal spinous and granular layers as they differentiate, and finally into the terminally differentiated outermost stratum corneum. Keratinocytes undergoing terminal differentiation are marked by tissue-specific concomitant expression of genes encoded in the Epidermal Differentiation Complex (EDC) locus. The EDC genes are organized into four gene families - S100, Sprr, Lce, and Flg-like, which are coordinately expressed upon activation of the terminal differentiation program in keratinocytes. The molecular mechanisms that govern the activation of the EDC during epidermal differentiation are poorly understood. The synteny and colinearity of the locus across multiple mammalian species and the coordinate expression of EDC genes upon keratinocyte differentiation suggest molecular mechanisms operating at the chromatin level. I hypothesize coordinate activation of the EDC by an enhancer regulatory element. Enhancers are non-coding regulatory DNA sequences that upon binding specific transcription factors, are able to increase expression of a proximal or distal target gene. Previous work in our lab identified an epidermal-specific enhancer, CNE 923, that was active in in cell-based luciferase assays and transgenic mice. Here, I examine the function of the 923 enhancer for epidermal differentiation. Using an independent transgenic mouse line, I identified spatiotemporal sensitivity of the 923 enhancer that correlated with the patterning of epidermal barrier formation during mouse embryonic development. To determine if 923 formed chromatin interactions with the EDC gene promoters, I performed chromosome conformation capture (3C) assays in proliferating and differentiated primary mouse keratinocytes. The 3C studies identified physiologically sensitive chromatin interactions between 923 and EDC gene promoters. The data supports a dynamic EDC chromatin topology during keratinocyte differentiation. A requirement for c-Jun/AP-1 in relation to 923-mediated EDC chromatin remodeling for normal EDC gene expression during keratinocyte differentiation was further determined by chromatin immunoprecipitation, 3C, and RNA-seq upon pharmacological inhibition of AP-1 binding. To further determine the function of 923 in vivo, I generated a series of mutation alleles using CRISPR/Cas9 genome editing in mice. Cas9 nuclease activity targeted to the flanking ends of the 923 enhancer in mouse zygotes by a pair of guide RNAs, coupled with homologous recombination-mediated loxP insertions, generated 1 floxed (923flox), 2 independent deletions (923delA, 923delB), and 1 partial deletion (923pdel) alleles for the 923 enhancer. My results from the 923 knockout mice identified decreased expression of nearby Smcp, Lce6a, and involucrin gene expression, decreased distal Crnn and Lce gene family members, and a correlative increase in expression of Sprr gene family members. To identify the chromatin interactions for the 923 enhancer on a genome-wide scale, I performed high-throughput circular chromosome conformation capture (4C-seq) assays with respect to the 923 enhancer and an additional Flg promoter viewpoint in proliferating and differentiated keratinocytes and P5424 T-cells. My results revealed 923 enhancer-mediated chromatin interactions indicative of a topologically associated domain encompassing the EDC. However, an enrichment of 923 mediated chromatin interactions within the EDC, were identified in keratinocytes relative to the T-cells, specifically between the 923 enhancer and the Sprr and Lce gene families, and with non-coding regions in the gene desert between the S100 and Sprr gene families. Of note was a 923 interaction with another putative enhancer near Crct1, enriched specifically in proliferating keratinocytes, and suggesting cross-talk between enhancers. Keratinocyte-specific trans-interactions identified by MACS and GREAT algorithms included genes important for epidermal function including Trp63, an important regulator of keratinocyte differentiation. Together, my 4C-seq identifies unique chromatin architectures of the EDC in keratinocytes and T cells, including keratinocyte-specific enhancer-enhancer crosstalk in cis and interactions between transcriptionally active loci in trans. My studies identify, for the first time, a link between the 923 enhancer and proximal (Ivl, Smcp, Lce6a) and distal genes (Crnn, distal Lce family), the loss of which coincides with upregulation of other epidermal differentiation genes (Sprr family) to maintain skin barrier function. Together, my work has identified 923 as an epidermal-specific enhancer that participates in a chromatin looping network to co-regulate expression of genes important for epidermal development, as a mechanism for maintaining skin barrier integrity

Methods for Joint Normalization and Comparison of Hi-C data

The development of chromatin conformation capture technology has opened new avenues of study into the 3D structure and function of the genome. Chromatin structure is known to influence gene regulation, and differences in structure are now emerging as a mechanism of regulation between, e.g., cell differentiation and disease vs. normal states. Hi-C sequencing technology now provides a way to study the 3D interactions of the chromatin over the whole genome. However, like all sequencing technologies, Hi-C suffers from several forms of bias stemming from both the technology and the DNA sequence itself. Several normalization methods have been developed for normalizing individual Hi-C datasets, but little work has been done on developing joint normalization methods for comparing two or more Hi-C datasets. To make full use of Hi-C data, joint normalization and statistical comparison techniques are needed to carry out experiments to identify regions where chromatin structure differs between conditions. We develop methods for the joint normalization and comparison of two Hi-C datasets, which we then extended to more complex experimental designs. Our normalization method is novel in that it makes use of the distance-dependent nature of chromatin interactions. Our modification of the Minus vs. Average (MA) plot to the Minus vs. Distance (MD) plot allows for a nonparametric data-driven normalization technique using loess smoothing. Additionally, we present a simple statistical method using Z-scores for detecting differentially interacting regions between two datasets. Our initial method was published as the Bioconductor R package HiCcompare [http://bioconductor.org/packages/HiCcompare/](http://bioconductor.org/packages/HiCcompare/). We then further extended our normalization and comparison method for use in complex Hi-C experiments with more than two datasets and optional covariates. We extended the normalization method to jointly normalize any number of Hi-C datasets by using a cyclic loess procedure on the MD plot. The cyclic loess normalization technique can remove between dataset biases efficiently and effectively even when several datasets are analyzed at one time. Our comparison method implements a generalized linear model-based approach for comparing complex Hi-C experiments, which may have more than two groups and additional covariates. The extended methods are also available as a Bioconductor R package [http://bioconductor.org/packages/multiHiCcompare/](http://bioconductor.org/packages/multiHiCcompare/). Finally, we demonstrate the use of HiCcompare and multiHiCcompare in several test cases on real data in addition to comparing them to other similar methods (https://doi.org/10.1002/cpbi.76)

Genome architecture: from linear organisation of chromatin to the 3D assembly in the nucleus

The genetic information is stored in the eukaryotic nucleus in the form of chromatin. This is a macromolecular entity that includes genomic DNA and histone proteins that form nucleosomes, plus a large variety of chromatin-associated non-histone proteins. Chromatin is structurally and functionally organised at various levels. One reveals the linear topography of DNA, histones and their post-translational modifications and non-histone proteins along each chromosome. This level provides regulatory information about the association of genomic elements with particular signatures that have been used to define chromatin states. Importantly, these chromatin states correlate with structural and functional genomic features. Another regulatory layer is established at the level of the 3D organisation of chromatin within the nucleus, which has been revealed clearly as non-random. Instead, a variety of intra- and inter-chromosomal genomic domains with specific epigenetic and functional properties has been identified. In this review, we discuss how the recent advances in genomic approaches have contributed to our understanding of these two levels of genome architecture. We have emphasised our analysis with the aim of integrating information available for yeast, Arabidopsis, Drosophila, and mammalian cells. We consider that this comparative study helps define common and unique features in each system, providing a basis to better understand the complexity of genome organisation.Ministerio de Economía y Competitividad (grant BFU2012–34821) and an institutional grant of Fundación Ramón Areces to the Centro de Biología Molecular Severo Ochoa.Peer Reviewe

Bioinformatic analyses of the structural and functional complexity in chromosomal interactomes

Evolution requires information storage systems with different demands with respect to persistence. While the genome provides a mechanism for long term, static and accurate information storage, it is incapable of mediating adaptation to short term changes in the environment. Chromatin, however, constitutes a dynamic, reprogrammable memory with different levels of persistence. Moreover, chromatin states carry information not only in 2D, i.e. in the structure of the primary chromatin fibre, but also in the 3D organization of the genome in the nuclear space. The following thesis delves into the new bioinformatic and wet lab protocols developed to map, quantitative and functionally analyze the 3D architecture of chromatin. The chromatin insulator protein CTCF is a major factor underlying the 3D organization of the epigenome. We have uncovered, however, that CTCF binding sites within a regulatory region have multiple functions that are influenced by the chromatin environment and possibly the combinatorial usage of the 11 Zn-fingers of CTCF (Paper I). This observation exemplifies that understanding the function of dynamic and transient chromatin fibre interactions requires novel technology that enables the detection of 3D chromatin folding with high resolution in single cells and in small cell populations. We therefore set out to devise a novel method for the visualization of higher order chromatin structures by combining the strengths of both DNA Fluorescent In Situ Hybridization (FISH) and In Situ Proximity Ligation Assay (ISPLA) technologies (Paper II). The resulting Chromatin in Situ Proximity (ChrISP) assay thus takes advantage of the direct contact detection of ISPLA and the locus-specific nature of FISH and uncovered the existence of compact chromatin structures at the nuclear envelope with unprecedented resolution. To complement ChrISP with a high throughput method capable of quantitatively recovering chromatin fibre contacts in small cell populations, we furthermore innovated the Nodewalk assay (Paper III). The protocol builds on existing ligation based chromosome conformation capture methods, but features significant reduction in the random ligation event frequency, inclusion of negative and positive ligation controls, iterative template resampling, increased signal to noise ratio and improved sensitivity. Using this technique, we have uncovered a cancer cell-specific, productive chromatin fibre interactome connecting the promoter and enhancer of c-MYC to a network of enhancers and super-enhancers. Underpinning this new protocol, I have developed the Nodewalk Analysis Pipeline (NAP) (Paper IV). This suite of tools consists of preprocessing, analysis and post-processing modules designed specifically for the rapid and efficient analysis of Nodewalk datasets through an interactive and user-friendly web based interface. Overall the work described in this thesis advances our understanding of the role of CTCF in nuclear organization and provides innovative wet lab techniques along with specialized software tools. Moreover, this work is an example of an emerging trend where the challenge of understanding chromatin dynamics within the 3D nuclear architecture demands a close synergistic collaboration between the fields of biology, biotechnology and bioinformatics

Data mining and machine learning methods for chromosome conformation data analysis

Sixteen years after the sequencing of the human genome, the Human Genome Project (HGP), and 17 years after the introduction of Chromosome Conformation Capture (3C) technologies, three-dimensional (3-D) inference and big data remains problematic in the field of genomics, and specifically, in the field of 3C data analysis. Three-dimensional inference involves the reconstruction of a genome's 3D structure or, in some cases, ensemble of structures from contact interaction frequencies extracted from a variant of the 3C technology called the Hi-C technology. Further questions remain about chromosome topology and structure; enhancer-promoter interactions; location of genes, gene clusters, and transcription factors; the relationship between gene expression and epigenetics; and chromosome visualization at a higher scale, among others. In this dissertation, four major contributions are described, first, 3DMax, a tool for chromosome and genome 3-D structure prediction from H-C data using optimization algorithm, second, GSDB, a comprehensive and common repository that contains 3D structures for Hi-C datasets from novel 3D structure reconstruction tools developed over the years, third, ClusterTAD, a method for topological associated domains (TAD) extraction from Hi-C data using unsupervised learning algorithm. Finally, we introduce a tool called, GenomeFlow, a comprehensive graphical tool to facilitate the entire process of modeling and analysis of 3D genome organization. It is worth noting that GenomeFlow and GSDB are the first of their kind in the 3D chromosome and genome research field. All the methods are available as software tools that are freely available to the scientific community.Includes bibliographical reference