Chromatin 3D modelling from sparse 3C-based datasets

Genome spatial organisation and transcriptional activity are tightly coordinated to ensure the correct function of the cell. Thus, proper understanding of the chromatin organisation is needed to deepen into the processes regulating the activity of specific loci of interest. In this matter, Chromatin Conformation Capture (3C)-based technologies have helped to increase the understanding of the genomic interaction landscape. Particularly, sparse 3C technologies, like promoter capture Hi-C (pcHi-C), have focused on specific interactions of interest to unveil the interaction landscape associated with functional elements, like promoters. However, to properly characterize the sparse interaction profiles of pcHi-C, it is important to contextualize these interactions in a 3D perspective. Hence, in this thesis, we have developed a tool for the 3D modelling and analysis of sparse 3C-based datasets like pcHi-C, and we have probed its utility to help interpreting the regulatory architecture surrounding genes associated with cell-type or tissue-specific activLa organización espacial del genoma y la actividad transcripcional están estrechamente coordinadas para garantizar el correcto funcionamiento de la célula. Por lo tanto, se necesita una comprensión adecuada de la organización de la cromatina para profundizar en los procesos que regulan la actividad de loci de interés. Tecnologías basadas en la captura de conformación de cromatina (3C) han facilitado la comprensión de la arquitectura genómica. Particularmente, las tecnologías 3C sparse, como promoter capture Hi-C (pcHi-C), se han centrado en interacciones especificas de interés para desvelar el panorama de interacción asociado con elementos funcionales como los promotores. Sin embargo, para comprender adecuadamente los perfiles sparse de interacción de pcHi-C, es importante contextualizar la perspectiva 3D que subyace a estas interacciones. En esta tesis, hemos desarrollado una herramienta para el modelado y análisis 3D de datos sparse derivados de 3C como pcHi-C, y hemos probado su utilidad en la comprensión de la arquitectura reguladora de genes asociados con una actividad especifica del tipo celular o tejido

3D reconstruction of genomic regions from sparse interaction data

Chromosome conformation capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterize short- and long-range enhancer-promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organization from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalization, detection of significant interactions and reconstruction of the full 3D organization of the genomic region despite of the data sparseness. Specifically, it builds, with as low as the 2-3% of the data from the matrix, reliable 3D models of similar accuracy of those based on dense interaction matrices. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organizational features such as the formation of different networks of active gene communities.This study makes use of data generated by the PCHI-C Consortium available in the EGA European Genome-Phenome Archive (National Institute for Health Research of England, UK Medical Research Council (MR/L007150/1) and UK Biotechnology and Biological Research Council (BB/J004480/1

Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes

Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer clusters or super-enhancers. So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in three-dimensional (3D) space. Furthermore, their target genes are often unknown. We have created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers to their target genes, often located hundreds of kilobases away. It also revealed >1,300 groups of islet enhancers, super-enhancers and active promoters that form 3D hubs, some of which show coordinated glucose-dependent activity. We demonstrate that genetic variation in hubs impacts insulin secretion heritability, and show that hub annotations can be used for polygenic scores that predict T2D risk driven by islet regulatory variants. Human islet 3D chromatin architecture, therefore, provides a framework for interpretation of T2D genome-wide association study (GWAS) signals.This research was supported by the National Institute for Health Research Imperial Biomedical Research Centre. Work was funded by grants from the Wellcome Trust (nos. WT101033 to J.F. and WT205915 to I.P.), Horizon 2020 (Research and Innovation Programme nos. 667191, to J.F., 633595, to I.P., and 676556, to M.A.M.-R.; Marie Sklodowska-Curie 658145, to I.M.-E., and 43062 ZENCODE, to G.A.), European Research Council (nos. 789055, to J.F., and 609989, to M.A.M.-R.). Marató TV3 (no. 201611, to J.F. and M.A.M.-R.), Ministerio de Ciencia Innovación y Universidades (nos. BFU2014-54284-R, RTI2018-095666, to J.F., BFU2017-85926-P, to M.A.M.-R., IJCI-2015-23352, to I.F.), AGAUR (to M.A.M.-R.). UK Medical Research Council (no. MR/L007150/1, to P.F., MR/L02036X/1 to J.F.), World Cancer Research Fund (WCRF UK, to I.P.) and World Cancer Research Fund International (no. 2017/1641 to I.P.), Biobanking and Biomolecular Resources Research Infrastructure (nos. BBMRI-NL, NWO 184.021.007, to I.O.F.). Work in IDIBAPS, CRG and CNAG was supported by the CERCA Programme, Generalitat de Catalunya and Centros de Excelencia Severo Ochoa (no. SEV-2012-0208). Human islets were provided through the European islet distribution program for basic research supported by JDRF (no. 3-RSC-2016-160-I-X). We thank N. Ruiz-Gomez for technical assistance; R. L. Fernandes, T. Thorne (University of Reading) and A. Perdones-Montero (Imperial College London) for helpful discussions regarding Machine Learning approaches; B. Lenhard and M. Merkenschlager (London Institute of Medical Sciences, Imperial College London), F. Müller (University of Birmingham) and J. L. Gómez-Skarmeta (Centro Andaluz de Biología del Desarrollo) for critical comments on the draft; the CRG Genomics Unit; and the Imperial College High Performance Computing Service

CTCF is dispensable for immune cell transdifferentiation but facilitates an acute inflammatory response

Three-dimensional organization of the genome is important for transcriptional regulation1-7. In mammals, CTCF and the cohesin complex create submegabase structures with elevated internal chromatin contact frequencies, called topologically associating domains (TADs)8-12. Although TADs can contribute to transcriptional regulation, ablation of TAD organization by disrupting CTCF or the cohesin complex causes modest gene expression changes13-16. In contrast, CTCF is required for cell cycle regulation17, embryonic development and formation of various adult cell types18. To uncouple the role of CTCF in cell-state transitions and cell proliferation, we studied the effect of CTCF depletion during the conversion of human leukemic B cells into macrophages with minimal cell division. CTCF depletion disrupts TAD organization but not cell transdifferentiation. In contrast, CTCF depletion in induced macrophages impairs the full-blown upregulation of inflammatory genes after exposure to endotoxin. Our results demonstrate that CTCF-dependent genome topology is not strictly required for a functional cell-fate conversion but facilitates a rapid and efficient response to an external stimulus.We thank M. T. Kanemaki for the degron plasmids; R. Guigó’s laboratory, and S. Pérez-Lluch in particular, for the H3K27ac and H3K4me1 ChIP–seq, produced in the framework of the RNA-MAPS project (ERC-2011-AdG-294653-RNA-MAPS); Y. Cuartero for help with sequencing and CTCF ChIP–seq; C. Segura for help with immunofluorescence microscopy; the CRG Genomics and flow cytometry core facilities and the CRG-CNAG Sequencing Unit for sequencing; and members of T.G.’s laboratory for discussions. This work was supported by the European Research Council under the 7th Framework Programme FP7/2007-2013 (ERC Synergy Grant 4D-Genome, grant agreement 609989, to T.G. and M.A.M.-R.), the Ministerio de Educación y Ciencia (SAF.2012-37167, to T.G., and BFU2017-85926-P, to M.A.M.-R.), the AGAUR (to T.G.) and the Marató TV3 (201611) (to M.A.M.-R.). P.C. was supported by the Deutsche Forschungsgemeinschaft (SFB860, SPP1935, EXC 2067/1-390729940), the European Research Council (advanced investigator grant TRANSREGULON, grant agreement no. 693023) and the Volkswagen Foundation