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Two independent modes of chromatin organization revealed by cohesin removal

Nature volume 551, pages 51–56 (2017)Cite this article

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Abstract

Imaging and chromosome conformation capture studies have revealed several layers of chromosome organization, including segregation into megabase-sized active and inactive compartments, and partitioning into sub-megabase domains (TADs). It remains unclear, however, how these layers of organization form, interact with one another and influence genome function. Here we show that deletion of the cohesin-loading factor Nipbl in mouse liver leads to a marked reorganization of chromosomal folding. TADs and associated Hi-C peaks vanish globally, even in the absence of transcriptional changes. By contrast, compartmental segregation is preserved and even reinforced. Strikingly, the disappearance of TADs unmasks a finer compartment structure that accurately reflects the underlying epigenetic landscape. These observations demonstrate that the three-dimensional organization of the genome results from the interplay of two independent mechanisms: cohesin-independent segregation of the genome into fine-scale compartments, defined by chromatin state; and cohesin-dependent formation of TADs, possibly by loop extrusion, which helps to guide distant enhancers to their target genes.

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Figure 1: Overview of the experimental design.
Figure 2: Nipbl deletion leads to disappearance of TADs and peaks from Hi-C contact maps.
Figure 3: Nipbl deletion leads to activity-dependent alteration of compartment structure.
Figure 4: Transcriptional changes in Nipbl mutants reveal possible enhancer–promoter miscommunication in absence of TADs.
Figure 5: Two independent but overlapping modes of chromosomal 3D organization.

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Acknowledgements

This work would not have been possible without the contribution of the members of the EMBL Laboratory Animal Resources Facility, particularly S. Feller, to animal welfare and husbandry. We thank members of the Mirny and the Spitz labs, J. Marioni (EMBL/EBI)) for discussions and suggestions, H. Marlow for help and expertise in generating the cohesin and CTCF ChIP–seq data, the EMBL Genomics Core Facility and Pasteur Biomics for assistance and support in sequencing the genomic libraries, and A. Losada for providing antibodies. W.S. and A.P. were supported by an EMBL Interdisciplinary Postdoc (EIPOD) Fellowship under Marie Curie Actions COFUND. The work in the Mirny laboratory is supported by R01 GM114190, U54 DK107980 from the NIH, and 1504942 from the NSF. The collaboration is also partially supported by the MIT-France MISTI Fund. The work in the Spitz laboratory was supported by EMBL, the Pasteur Institute and the Deutsche Forschungsgesellschaft (DFG grant: SP 1331/3-1) and included funding from the European Commission’s Seventh Framework Programme through the Collaborative Research Project RADIANT (grant agreement no. 305626, to W.H.).

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Author notes

  1. Wibke Schwarzer, Nezar Abdennur and Anton Goloborodko: These authors contributed equally to this work.

Authors and Affiliations

  1. Developmental Biology Unit. European Molecular Biology Laboratory, Heidelberg, 69117, Germany

    Wibke Schwarzer & Francois Spitz

  2. Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Nezar Abdennur

  3. Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Anton Goloborodko & Leonid Mirny

  4. Genome Biology Unit. European Molecular Biology Laboratory, Heidelberg, 69117, Germany

    Aleksandra Pekowska, Wolfgang Huber & Francois Spitz

  5. Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Geoffrey Fudenberg & Leonid Mirny

  6. Developmental and Stem Cell Biology Department, Institut Pasteur, (Epi)genomics of Animal Development Unit, Institut Pasteur, 75015, Paris, France

    Yann Loe-Mie & Francois Spitz

  7. CNRS, UMR3738, 25 rue du Dr Roux, Paris, 75015, France

    Yann Loe-Mie & Francois Spitz

  8. European Bioinformatics Institute. European Molecular Biology Laboratory. Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK

    Nuno A Fonseca

  9. Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, 69117, Germany

    Christian H. Haering

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Contributions

W.S. and F.S. conceived the study and designed the experiments, with input and advice from C.H.H. Experimental data were generated by W.S., with the help of A.P. for TCC; A.P. also carried out preliminary analyses of the TCC datasets, with advice from W.H. N.A. and A.G. performed computational analysis of Hi-C, RNA-seq, ChIP–seq and other relevant datasets. Y.L.-M. and N.A.F. contributed to analysis of transcription data. G.F. performed computer simulations of cohesin depletion and assisted with data analysis and paper writing. L.M. provided advice on data analysis and simulations. W.S., N.A., A.G., L.M. and F.S. wrote the paper with input from the other authors.

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Correspondence to Leonid Mirny or Francois Spitz.

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Reviewer Information Nature thanks K. Shirahige and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Overview of various features of chromosomal architecture detected and quantified in Hi-C contact maps.

Top row, intra-chromosomal maps show the decay of contact frequency with genomic distance, which can be quantified with the curves of contact frequency P(s) versus genomic separation s. Middle row, both intra- and inter-chromosomal maps display a checkered pattern caused by compartmentalization of the genome. This pattern can be quantified by a continuous genomic track obtained via eigenvector decomposition of either cis or trans maps. Bottom row, intra-chromosomal maps at short genomic distance scales reveal domains of enriched contact frequency, which appear as bright squares along the main diagonal, and peaks which appear as bright dots connecting two loci. Both can be detected and quantified using specialized algorithms.

Extended Data Figure 2 Conditional inactivation of Nipbl in mice.

a, Schematic representation of the conditional allele, with loxP sites (red triangles) flanking exon 18. The reading frame of each exon is indicated below the corresponding square, as x–x. Deletion of exon 18 leads to a frame-shift introducing a premature stop codon (indicated by amino acids in red). The resulting protein lacks the critical HEAT domains that are conserved in NIPBL and SCC2 proteins. b, c, Embryonic day 12 (E12) embryos (b) and E18 fetuses (c) carrying the conditional Nipbl allele (Nipblflox) and either ubiquitous (Hprt:Cre45) or limb-specific (Prx1::Cre46) Cre recombinase drivers. Structures expressing Cre are rapidly lost in Nipblflox/flox animals. Heterozygous Nipblflox/+ animals are grossly morphologically normal, but die soon after birth, as reported for other Nipbl loss of function alleles68. Fl, forelimb; md, mandible; abw, abdominal wall. d, e, Histochemical staining of liver section of adult ΔNipbl mouse (Nipblflox/flox; Ttr::CreERT2; 10 days after tamoxifen injection) for a proliferation marker (Phos-H3) (d) and apoptosis (cleaved Caspase3) (e) (both shown in red). Nuclei are stained with DAPI (blue). Staining was performed once.

Extended Data Figure 3 Calibrated ChIP–seq for CTCF and cohesin (Rad21 and SMC3).

a, Left, comparison of calibrated mouse ChIP–seq data. For each factor (CTCF, Rad21, SMC3), we used the ratio of the top 0.2% of 200-bp bins (~29,000 points) in the TAM hg19 fraction vs the ΔNipbl hg19 fraction to rescale the mm9 signal in the ΔNipbl condition in order to be compared with the mm9 signal in the TAM condition (see Methods). The scatter plot heatmaps in the left column are shaded by point density. Right, log2 fold difference ratio between ΔNipbl and TAM for ChIP–seq peaks (points with ChIP in TAM > 2.0). While the calibrated CTCF binding signal stays relatively constant between conditions (within the twofold envelope), most of the calibrated cohesin binding signal drops by about 2–8-fold in ΔNipbl cells, with a mean depletion of 3.7-fold. b, Uncalibrated ChIP–seq reads used for the calibration of human cell (hg19, left) and mouse (mm9, right). Processed mapped reads were converted to genome-wide signal tracks binned at 200-bp resolution. Scatter plots of these genomic tracks (TAM vs ΔNipbl) are shown as heatmaps shaded by point density. For cohesin subunits, the hg19 signal has a similar profile in both conditions, but the ΔNipbl signal is diminished in the uncalibrated mm9 fraction. The uncalibrated mm9 signal also appears to be reduced in ΔNipbl for CTCF; however, a similar diminishing effect is seen in the hg19 data. c, Stacked heatmaps of calibrated ChIP–seq signal at the top 20,000 CTCF binding sites (peaks with an assigned CTCF motif), ranked by fold change over input in the TAM control condition. d, Scatter plots of calibrated ChIP–seq tracks as in b, but split into two groups by compartment type A (compartment eigenvector >0) and B (eigenvector assignment <0). The shading is coloured by the eigenvector signal. Black and green dashed diagonal lines demarcate the twofold and eightfold envelopes, respectively. e, Total cohesin occupancy. Bar plot showing the number of ChIP–seq 200-bp points in the non-shaded area of the scatter plots (bins with TAM signal >2) representing high confidence binding signal in the wild type. While cohesin binding is more than threefold more prevalent in A-compartment regions, the scatter plots show that both A and B regions respond equally to Nipbl deletion.

Extended Data Figure 4 Deletion of Nipbl in this study leads to more robust disappearance of TADs and associated peaks than in previous publications.

a, Genetic deletion of Nipbl in hepatocytes (this study). b, Deletion of Rad21 in thymocytes20. c, Proteolytic cleavage of RAD21 in HEK293T cells19. d, e, Deletion of Rad21 in NSCs and astrocytes (ASTs)21. For each data set: top, average Hi-C map around 102 peaks with size range 500–600 kb12 in wild-type and ΔNipbl contact maps; middle, average Hi-C map of TADs called in each data set; bottom, relative contact probability between pairs of peak loci versus genomic distance, compared to randomly selected pairs of loci. The thick line shows the median contact probability; the shading shows the envelope between the 25th and 75th percentiles of contact probability at each genomic separation. Note substantial change in the contact probability at peaks (red line) upon Nipbl depletion (a) and little change in other studies (b–e). All studies used comparable Hi-C sequencing depth (Supplementary Table 3).

Extended Data Figure 5 Residual structures are observed in active and repressed regions of the genome after Nipbl deletion.

For each TAD, an activity was assigned based on the dominant simplified ChromHMM state category. a, Average contact map of 300–400 kb TADs in inert, repressed and active regions of the genome. b, Average contact map of 300–700 kb peaks in inert, repressed and active regions of the genome. c, Average contact maps of most upregulated 20% (left) and most downregulated 20% (right) of 300–400 kb TADs. d, e Compartmentalization saddle plots in cis (d) and trans (e): average interaction frequencies between pairs of loci (100-kb bins) arranged by their compartment signal (eigenvector value). The interaction frequencies in cis (top row) are computed for observed and expected contact maps. Notice enrichment of AA and depletion of AB interactions in ΔNipbl cells. Histograms along the axes show the distributions of eigenvector values.

Extended Data Figure 6 Average Hi-C contact footprint of CTCF sites.

CTCF peaks were detected in our ChIP–seq data from TAM control cells and supported by an underlying CTCF binding motif occurrence. a, Average iteratively corrected 20-kb resolution contact map of around 42,000 CTCF peaks in TAM and ΔNipbl cells. Individual contributing snippets to the composite heatmap were oriented such that the CTCF motif points in the direction shown by the grey arrow. b, Average 20-kb resolution contact map normalized by the expected contact frequency at a given genomic separation. c, Average observed over expected contact frequency curves along ‘slices’ of the composite heatmaps depicted by dashed grey lines in b: left, insulation profile at 200 kb separation (diagonal dashed line in b); right, ‘virtual 4C’ curve (vertical dashed line in b) of the composite heatmap. d, Average 10-kb resolution observed over expected contact map centred on about 11,000 pairs of CTCF ChIP–seq peaks with convergent motif orientations separated by 200 ± 10 kb.

Extended Data Figure 7 Fragmentation of A and B compartment-type in smaller alternating regions upon Nipbl deletion is activity-dependent.

a, Example region (chr3: 35–60 Mb) illustrating lack of compartment fragmentation in predominantly B-type regions with robust disappearance of TADs. Top, compartment eigenvector; bottom, contact matrix snapshot. b, Autocorrelation of eigenvector tracks reveals genome-wide fragmentation of active compartments. Left, genome-wide Spearman coefficient of correlation of the 20-kb cis eigenvector values (n = 113,372) of pairs of loci as a function of their genomic separation (autocorrelation). Top right, eigenvector correlation of locus pairs split by quintile of the eigenvector value of the upstream locus. Bottom right, chromosome-wide values of eigenvector correlation of locus pairs separated by 1 Mb. c, Spearman coefficient of correlation between the smoothed histone ChIP–seq, transcription factor ChIP–seq and RNA-seq tracks and the 20-kb cis eigenvectors (n = 113,372) as a function of the smoothing window size. Left group of panels show ENCODE data, right show data from this study. First and second rows, histone marks; third row, RNA-seq tracks; fourth row, miscellaneous tracks (DNase hypersensitivity, CTCF and PolII ChIP–seq and GC content). ΔNipbl eigenvectors show an increased correlation with tracks associated with transcriptional activity but a decreased correlation with the repression-associated track of H3K27me3 and GC content. d, Example of a large continuous wild-type (top) A-type compartment region (chr13: 45–48 Mb) and the same region in ΔNipbl (bottom). ΔNipbl compartment transitions highlighted by black dashed lines. TAD boundaries in the wild type are shifted or lost and replaced by compartment transitions in ΔNipbl cells. Below, histone ChIP–seq tracks69 and stranded RNA-seq tracks (blue, TAM hepatocytes; red, ΔNipbl cells) highlight that wild-type and TAM TADs do not strictly follow the underlying chromatin activities, whereas the new checkered pattern in ΔNipbl cells delineated by dashed lines correspond precisely to active versus inactive chromatin domains. In a and d, both replicates of each condition show similar results.

Extended Data Figure 8 TADs and compartments constitute independent layers of genome organization.

a, The residual contact-insulating boundaries in ΔNipbl cells are associated with compartment transitions. The first group of columns considers the boundaries detected in wild-type cells only, the second pair considers boundaries detected in both wild-type and ΔNipbl cells, and the last pair considers boundaries detected in ΔNipbl cells only. For each group, the first, second and third columns display data (eigenvectors and Hi-C) from wild-type, TAM and ΔNipbl cells, respectively. Within each column the top row is a stack of eigenvector tracks in a ±500-kb window around boundaries, oriented such that the left half of the window has greater average signal value and sorted by the average wild-type eigenvector value in the window. The second row shows a density histogram of eigenvector values as a function of the distance to the boundary. The third and fourth rows show the boundary-centred average contact probability and observed/expected contact ratio, respectively. The density histograms show that common and ΔNipbl-specific boundaries correspond to sharp transitions of compartment signals in ΔNipbl cells, in contrast to the more diffuse signal at these positions in wild-type and TAM cells. b, Boundaries of former TADs and new compartment domains do not coincide. Examples of TADs detected in wild-type cells, which contain sharp compartment transitions revealed by ΔNipbl contact maps. Left, TAM control data; right, ΔNipbl data. Top of each figure, local compartment signal in the corresponding cell type. The contact maps are centred at the sharp compartment transition in ΔNipbl. These examples illustrate that that chromatin-bound cohesins can interfere locally with genome compartmentalization. c, d, New compartments do not respect TAD boundaries but do respect the underlying chromatin domains. A large region (chr16: 50,420,000–54,420,000) adopts a very different 3D organization in control (c, in blue) and ΔNipbl cells (d, in red). Hi-C data are shown, as well as the eigenvector values in the two conditions. RNA-seq tracks showed minimal changes of expression (Alcam expression is reduced twofold in ΔNipbl cells) and chromatin states. ChIP–seq tracks for H3K27ac and H3K4me3 are shown in the two conditions, with log2 ratio tracks under the ΔNipbl (d) panel. Encode tracks (corresponding to wild-type liver cells) are shown in the grey boxed area. The new structure adopted in ΔNipbl cells put together the two active genes that are normally in different TADs in the same domain, corresponding to the active chromatin linear domain. In b–d, both replicates of each condition showed similar results.

Extended Data Figure 9 Eigenvector change upon Nipbl deletion is activity-dependent and uncorrelated with changes in gene expression or epigenetic marks.

a, Correlation of cis eigenvector values of 100-kb genomic bins before and after Nipbl deletion, split by the functional state of chromatin. Top row, left to right: genome-wide relationship; bins showing constitutive lamin-B1 association across four mouse cell types (cLADs); bins showing variable (facultative) lamin-B1 association (fLADs); binds not showing any association (non LADs). Bottom row: bins assigned the inert ChromHMM simplified state; bins assigned the repressed state; bins assigned the active state. b, Scatter plot of genome-wide ChIP–seq signal binned at 200 bp in wild-type versus ΔNipbl cells. Top, H3K27ac; bottom, H3K4me3. c, Stacked heatmap of histone ChIP–seq signal over input ± 10 kb around putative TSS sites sorted by total H3K27ac signal in the wild type and oriented by TSS strand. From left to right: H3K4me3 in wild-type and ΔNipbl, followed by H3K27ac in wild-type and ΔNipbl. d, ChIP–seq signal for histone marks of activity versus eigenvector value of 20-kb bins. Top row, H3K27ac; bottom row, H3K4me3. Left column, wild-type cells; middle column, ΔNipbl cells; right column, correlation of changes in both signals upon Nipbl deletion. e, Change in compartment structure upon Nipbl deletion cannot be attributed to the sign of the local expression change. The heatmap shows the number of 100-kb genomic bins as a function of the ranks of expression change and the eigenvector change. The attached plots show the correspondence between the values of expression change (top) or eigenvector change (right) and their ranks.

Extended Data Figure 10 Expression changes in ΔNipbl hepatocytes.

a, Changes in gene expression between TAM controls and ΔNipbl liver cells (four replicates for each condition) analysed with DESeq262. Genes with significant changes in gene expression (FDR >0.05) are coloured red (upregulated, n = 487) or blue (downregulated, n = 637), with larger dots corresponding to genes with a fold-change of more than 3. b, Intergenic distances for the different categories of disregulated genes (with fold-change more than 3; upregulated, 268; downregulated, 350; unchanged, 15,055). Statistical differences determined by an unpaired two-tailed t-test. The differences between means were 50,020.40 (CI 95% = 27723.85–72316.95) and 52,185 (CI 95% = 21824–82547) for downregulated versus unchanged, and downregulated versus upregulated, respectively. c, Size distribution of the TADs observed in wild-type cells (lost in ΔNipbl cells) depending on the degree alteration of their transcriptional states. The size of TADs with transcriptional changes (red) is significantly larger than those that do not show transcriptional alterations (black) (Kolmogorov–Smirnov, P = 4.095 × 10−8). d, Change in transcription in non-genic intervals (including intergenic and antisense within gene bodies). Gene expression was calculated as the normalized number of reads within intervals defined by merging adjacent 1-kb windows showing readcounts over background (see Methods). The numbers of non-coding transcripts upregulated (in red) or downregulated (in blue) in ΔNipbl compared to the TAM control is given (P < 0.01 using a two-tailed t-test, four replicates per condition, fold-change higher than 8), with the second number indicating the high-confidence events (labelled with coloured dots, expression value over an arbitrary threshold of 30 reads) which constitute the list used for subsequent analyses. e, Comparison of control and ΔNipbl H3K27ac normalized signals within predicted liver enhancer elements (n = 51,850; readcounts within ±500 bp of predicted enhancer peak)69. f–i, Examples of transcriptional changes upon Nipbl deletion. Stranded RNA-seq and ChIP–seq tracks (H3K4me3, H3K27ac) are shown for control (blue) and ΔNipbl (red) samples. Comparison of the chromatin profiles are shown with log2[ΔNipbl/TAM] tracks for H3K4me3 and H3K27ac (in grey). Active enhancers (peaks of high H3K27ac, H3K4me1, low H3K4me3)69 are depicted as green ovals. f, Chr10: 21,090,000–21,781,000. Bidirectional transcription (position labelled with a blue bar) arises from an isolated enhancer in ΔNipbl cells. g, Chr17: 45,945,000–46,176000. Bidirectional transcription (position labelled with a blue bar) arises from two cryptic promoters (H3K4me3 peaks, no or weak transcription in TAM) downstream of Vegfa. h, Chr3: 21,712,500–22,126,240. A new transcript from a cryptic promoter 100 kb upstream of Tbl1xr1. H3K27ac signal is enhanced at peaks surrounding the activated cryptic promoter. i, Chr15: 9,873,000–10,354,700. Promoter switch for Prlr, from an upstream promoter to a more downstream one surrounded by active enhancers. j, chr6: 141,743,961–141,904,692. Downregulation of Slco1a1 and concomitant upregulation of Slco1a4 and noncoding intergenic transcripts (arrowheads). Distance of Slco1a4 promoter to intergenic enhancers is less than 10 kb, compared to 80 kb for Slco1a1.

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Schwarzer, W., Abdennur, N., Goloborodko, A. et al. Two independent modes of chromatin organization revealed by cohesin removal. Nature 551, 51–56 (2017). https://doi.org/10.1038/nature24281

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