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Highly interconnected enhancer communities control lineage-determining genes in human mesenchymal stem cells

Nature Genetics volume 52, pages 1227–1238 (2020)Cite this article

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Abstract

Adipocyte differentiation is driven by waves of transcriptional regulators that reprogram the enhancer landscape and change the wiring of the promoter interactome. Here, we use high-throughput chromosome conformation enhancer capture to interrogate the role of enhancer-to-enhancer interactions during differentiation of human mesenchymal stem cells. We find that enhancers form an elaborate network that is dynamic during differentiation and coupled with changes in enhancer activity. Transcription factors (TFs) at baited enhancers amplify TF binding at target enhancers, a phenomenon we term cross-interaction stabilization of TFs. Moreover, highly interconnected enhancers (HICE) act as integration hubs orchestrating differentiation by the formation of three-dimensional enhancer communities, inside which, HICE, and other enhancers, converge on phenotypically important gene promoters. Collectively, these results indicate that enhancer interactions play a key role in the regulation of enhancer function, and that HICE are important for both signal integration and compartmentalization of the genome.

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Fig. 1: Enhancer-capture Hi-C identifies functional chromatin interactions in hMSCs.
Fig. 2: The enhancer interactome is highly plastic and linked to enhancer activity at both ends of an interaction.
Fig. 3: Transcription factor cross-talk between connected enhancers.
Fig. 4: HICE engage in multiple types of chromatin interactions.
Fig. 5: HICE communities predict dynamic gene expression during adipogenesis.
Fig. 6: Regulation of HICE communities defines lineage choice.

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Data availability

All generated sequence data are available at the GEO repository under accession code GSE140782. In addition to the data generated for this study, raw sequencing data from single-cell and bulk RNA-seq, ChIP–seq and DNase I-seq were downloaded from the GEO under accession code GSE113253.

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Acknowledgements

This work was supported by grants from the Independent Research Fund Denmark (Sapere Aude Advanced grant no. 12-125524), the Danish National Research Foundation (DNRF grant no. 141) to the Center for Functional Genomics and Tissue Plasticity (ATLAS), the Novo Nordisk Foundation (Advanced Grant) and through grants to the Danish Diabetes Academy and NNF Center for Stem Cell Biology (NNF17CC0027852), the Villum Foundation (through support to the Villum Center for Bioanalytical Sciences) and the UKRI Medical Research Council (MR/L007150/1). We thank S. Andrews at the Babraham Institute for assistance with the initial probe design and our colleagues from the Functional Genomics and Metabolism Research Unit at the University of Southern Denmark for comments and discussions that greatly improved the manuscript.

Author information

Author notes

  1. Alexander Rauch

    Present address: Molecular Endocrinology & Stem Cell Research Unit, Department of Endocrinology and Metabolism, Odense University Hospital, Odense, Denmark

  2. Alexander Rauch

    Present address: Steno Diabetes Center Odense, Odense, Denmark

  3. Alexander Rauch

    Present address: Department of Clinical Research, University of Southern Denmark, Odense, Denmark

  4. Sofie Traynor

    Present address: Department of Cancer and Inflammation Research, Institute for Molecular Medicine, University of Southern Denmark, Odense, Denmark

  5. Anders K. Haakonsson

    Present address: Open Patient Data Explorative Network, Department of Clinical Research, University of Southern Denmark, Odense, Denmark

  6. Biola M. Javierre

    Present address: Josep Carreras Leukaemia Research Institute, Campus ICO-Germans Trias I Pujol, Barcelona, Spain

  7. Peter Fraser

    Present address: Department of Biological Science, Florida State University, Tallahassee, FL, USA

  8. These authors contributed equally: Maria S. Madsen, Alexander Rauch, Sofie Traynor.

Authors and Affiliations

  1. Functional Genomics & Metabolism Research Unit, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark

    Jesper G. S. Madsen, Maria S. Madsen, Alexander Rauch, Sofie Traynor, Elvira L. Van Hauwaert, Anders K. Haakonsson, Mette Hyldahl & Susanne Mandrup

  2. Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK

    Biola M. Javierre & Peter Fraser

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Contributions

Conceptualization: J.G.S.M., A.R., A.H.K., S.T. and S.M. Experimental work: M.S.M., A.R., S.T., E.L.V.H., B.M.J. Formal analysis, investigation and data curation: J.G.S.M. Visualization: J.G.S.M. and M.H. Writing: J.G.S.M. and S.M. Funding acquisition: S.M. Supervision: S.M. and P.F.

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Correspondence to Susanne Mandrup.

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P.F. is a cofounder of Enhanc3D Genomics. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Related to Figure 1.

a, Principal component analysis of all ECHi-C replicates and conditions (D0 = day 0, D1 = day 1, D10 = day 10). b, Stratum-adjusted correlation coefficients between all replicates and conditions (D0 = day 0, D1 = day 1, D10 = day 10). c, Fraction of all interactions detected at the indicated levels of significance at day 0 after combining the replicates (n0.01 = 398,606, n0.001 = 225,480, n0.0001 = 140,545 interactions) that are found in both individual replicates (at FDR ≤ 0.05). d, Histogram of the log2 absolute distances between significantly interacting fragments.

Extended Data Fig. 2 Related to Figure 2.

a, Virtual 4C plots of an induced (left) and a repressed (right) interaction. Line plots show log transformed loess smoothened normalized signal in a 200 kilobase window around the viewpoint. The black line represents the mean of the replicates, the shaded grey area shows the spread of the replicates and the dotted red line show the expected signal derived from CHiCAGO47. Heat maps show log transformed normalized signal for both replicates for each pooled fragment inside the window. Arcs show detected interactions (FDR ≤ 0.01). b, Enrichment of dynamic enhancer-to-enhancer interactions connecting enhancers where with no enhancers are co-regulated with the interaction, one of the enhancers are co-regulated or both of the enhancers are co-regulated relative to constant enhancer-to-enhancer interactions connecting enhancers where either neither enhancer is dynamically regulated (0, n = 70,181), one of the enhancer is dynamic (1, n = 42,441) or both of the enhancers are dynamic (2, n = 15,204). Dynamic regulation was defined as a significant change (FDR ≤ 0.05) in MED1 signal between day 0 and day 1 or 10. Co-regulation was defined as dynamic enhancers where MED1 changes in the same direction (gained or lost) as the interaction. c, Example of enhancers that interact in hMSC-TERT4 and for which an epigenomic QTL has been demonstrated in lymphoblastoid cells. Left panel: MED1 occupancy (normalized tag counts) and the interaction in undifferentiated hMSCs (day 0). Right panel: Effect of the genotype of the variant on the accessibility of the target site across lymphoblastoid cells11. (Ref = Homozygous for reference genotype, Het = Heterozygous, Alt = Homozygous for alternative genotype). (nRef = 32, nHet = 27, nAlt = 8).

Extended Data Fig. 3 Related to Figure 3.

a, Linear regression showing Loess smoothened occupancy (normalized tag counts) of the indicated factors measured using CUT&RUN at bound (≥ 4-fold over input, normalized tag count ≥ 25% quantile) baited enhancers as a function of occupancy at their target enhancers (within 200 kb). The black line shows the signal, and the shaded area the standard error. R2: Pearson’s correlation coefficient. α: The slope of the linear regression between occupancy (normalized tag counts) at either end using smoothened data. b, Boxplot showing the occupancy (normalized tag counts) for the indicated TFs at day 0 or day 1 and the absolute interaction distances for occupancy- and distance-matched baited enhancers (defined as in Fig. 3d).

Extended Data Fig. 4 Related to Figure 4.

a, Top 5 most enriched motifs in the top 1000 least connected regular enhancers versus the top 1000 most connected HICE at any time point during differentiation. b, Fraction of fragments from the indicated Hi-C libraries in different quality categories as defined by HiCUP62. c, Number of valid and captured read pairs after HiCUP quality filtering for each replicate of Hi-C in undifferentiated hMSCs (day 0). d, Boxplot indicating domain sizes of TADs and subTADs. Domains were detected as described in Fig. 4e. e, Example of a TAD and a subTAD that is delineated by an enhancer-to-enhancer interaction anchored in a HICE. DI = Directionality index. f, Fraction of baited enhancers strongly bound by CTCF (≥ 10 normalized tags, n = 1,147) in undifferentiated hMSCs (day 0). Baited enhancers were stratified by being HICE or regular as well as being boundary or non-boundary. Boundary enhancers were defined as enhancers harboring cross domain interactions (defined as in Fig. 4e). All other enhancers were defined as non-boundary. g, The number of CTCF sites with induced (green: FDR ≤ 0.05, logFC > 0, n = 2,028), unchanged (grey, n = 14,773) and repressed (red: FDR ≤ 0.05, logFC < 0 n = 1,577) CTCF occupancy for different groups of enhancers defined by the indicated change in number of detected enhancer-to-enhancer interactions between day 1 and day 0 of differentiation.

Extended Data Fig. 5 Related to Figure 5.

a, Boxplot indicating the number of enhancer-to-promoter interactions (E-P) for promoters in regular communities (defined as in Fig. 5a), or for promoters in HICE communities with an increasing number of interactions used as threshold for defining HICE. b, Boxplot indicating the number of enhancers per promoter in regular communities (defined as in Fig. 5a) or in communities defined by containing at least one HICE with an increasing number of interactions as threshold for defining HICE. c, Number of enhancer-to-promoter interactions (E-P) for enhancers in regular communities or in HICE communities (defined as in Fig. 5a). d, Overlap between HICE and baited super-enhancer constituents. Super-enhancers were identified with HOMER58 using the MED1 signal in DNase I hypersensitive sites at day 0, day 1 or day 10 of adipocyte differentiation. Constituents were defined as DHS sites overlapping with a super-enhancer identified at any time points. N denotes the number of enhancers. e, Enrichment of differentially expressed genes (maximum normalized tag count ≥ 1 and FDR ≤ 0.05, |log2FC| ≥ 1.5 at day 1 or 10 relative to undifferentiated hMSCs (day 0), n = 5,069) among genes contacted by both HICE communities and super-enhancer constituents (+HICE, +SE, n = 488), only HICE communities (+HICE, -SE, n = 346) or only super-enhancer constituents (-HICE, +SE, n = 1,160) relative to 1000 permutations of randomly selected genes. Data shown as mean ± SD. Dots show individual data points. f, Expression of markers of stem cells, preadipocytes or adipocytes in hMSC-TERT4 single cells6 ordered by pseudo-time calculated using Monocle54. g, Accuracy (Acc) and Kappa index of prediction of class labels (stem cell, preadipocytes or adipocytes based on pseudo-time) using a random forest model. The random forest model was trained on 80% of cells in each class and using all expressed genes. The accuracy and Kappa were evaluated on the remaining 20% of cells that was not used for training.

Extended Data Fig. 6 Related to Figure 6.

a, Fraction of fragments from the indicated Hi-C libraries in different quality categories as defined by HiCUP62. b, The number of valid and captured read pairs after HiCUP quality filtering for each in day 1 osteoblasts. c, The fraction of enhancer-to-enhancer (E-E, n = 109,333) and enhancer-to-promoter (E-P, n = 20,945) interactions that are significantly changed (FDR ≤ 0.05) between day 0 of differentiation and either day 1 in adipogenesis or day 1 in osteogenesis. d, The number of significant (FDR ≤ 1%) enhancer-to-enhancer and enhancer-to-promoter interactions in day 1 osteoblasts (Ob), undifferentiated hMSCs (D0) and day 1 adipocytes (Ad). e, Overlap of baits defined as HICE (baits with at least 8 enhancer-to-enhancer interactions) throughout early time points of adipogenesis and osteogenesis. N denotes the number of baits. f, Pie chart showing the number of enhancer communities (#promoters/community = 0) and gene regulatory communities (#promoters/community > 0). The bar plot shows the number of HICE gene regulatory communities (#HICE/community > 0) and regular gene regulatory communities (#HICE/community = 0). Communities were detected based on label propagation through a network constructed from all enhancers and promoters (nodes) and interactions (edges) weighted by their significance level. g, The log2 enrichment of enhancer-to-enhancer interactions located in the indicated clusters within HICE communities enriched for enhancer-to-promoter interactions from either cluster 2, 5 or 6 (as defined in Fig. 6b).

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Madsen, J.G.S., Madsen, M.S., Rauch, A. et al. Highly interconnected enhancer communities control lineage-determining genes in human mesenchymal stem cells. Nat Genet 52, 1227–1238 (2020). https://doi.org/10.1038/s41588-020-0709-z

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