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3D Enhancer–promoter networks provide predictive features for gene expression and coregulation in early embryonic lineages

Nature Structural & Molecular Biology volume 31, pages 125–140 (2024)Cite this article

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Abstract

Mammalian embryogenesis commences with two pivotal and binary cell fate decisions that give rise to three essential lineages: the trophectoderm, the epiblast and the primitive endoderm. Although key signaling pathways and transcription factors that control these early embryonic decisions have been identified, the non-coding regulatory elements through which transcriptional regulators enact these fates remain understudied. Here, we characterize, at a genome-wide scale, enhancer activity and 3D connectivity in embryo-derived stem cell lines that represent each of the early developmental fates. We observe extensive enhancer remodeling and fine-scale 3D chromatin rewiring among the three lineages, which strongly associate with transcriptional changes, although distinct groups of genes are irresponsive to topological changes. In each lineage, a high degree of connectivity, or ‘hubness’, positively correlates with levels of gene expression and enriches for cell-type specific and essential genes. Genes within 3D hubs also show a significantly stronger probability of coregulation across lineages compared to genes in linear proximity or within the same contact domains. By incorporating 3D chromatin features, we build a predictive model for transcriptional regulation (3D-HiChAT) that outperforms models using only 1D promoter or proximal variables to predict levels and cell-type specificity of gene expression. Using 3D-HiChAT, we identify, in silico, candidate functional enhancers and hubs in each cell lineage, and with CRISPRi experiments, we validate several enhancers that control gene expression in their respective lineages. Our study identifies 3D regulatory hubs associated with the earliest mammalian lineages and describes their relationship to gene expression and cell identity, providing a framework to comprehensively understand lineage-specific transcriptional behaviors.

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Fig. 1: Transcriptional changes and enhancer remodeling accompany early developmental decisions.
Fig. 2: Hi-C and H3K27ac HiChIP reveal multilayered 3D genomic reorganization and complex networks of putative regulatory interactions in TSCs, ESCs and XEN cells.
Fig. 3: Association of high 3D hubness with levels, cell-type specificity and coregulation of gene expression in early embryonic fates.
Fig. 4: Association of 3D rewiring with cell-type specific gene expression.
Fig. 5: Predictive modeling using 3D chromatin features outperforms promoter-based or 1D-based models for gene expression levels or cell-type specificity.
Fig. 6: Experimental validation of predicted enhancers in ESC and XEN.

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Data availability

All genomic datasets generated in this study (ChIP-seq, ChIP-exo, ATAC-seq, RNA-seq, 4C-seq, Hi-C and HiChIP) have been uploaded in the Gene Expression Omnibus (GEO) under accession number GSE213645. Source RT–qPCR data (normalized values) are provided along with all statistics in Supplementary Table 8.

Code availability

Custom R scripts used for data analysis in this study have been developed in our lab and are available upon request. The 3D-HiChAT code for calculating predicted gene expression and for scoring impactful enhancers is available on Github at https://github.com/Apostolou-Lab/3DHiChAT.

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Acknowledgements

We are grateful to all members of the Apostolou and Stadtfeld groups for critical reading of the manuscript and input. We also thank J. Pulecio and D. Huangfu for advice on the functional experiments and C. Leslie for advice on the modeling. Additionally, we are thankful to the reviewers for their insightful and constructive criticism to improve the manuscript. This work was partly supported by a HiChIP research grant from Arima Genomics. D.M. was supported by the T32 HD060600. E.A. is a recipient of the Mark Foundation Emerging Leader Award and supported by the National Institutes of Health (1R01GM138635, 1U01DK128852, RM1GM139738) and the Tri-Institutional Stem Cell Initiative of the Starr Foundation.

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Author notes

  1. Dafne Campigli Di Giammartino

    Present address: 3D Chromatin Conformation and RNA Genomics Laboratory, Center for Human Technologies (CHT), Istituto Italiano di Tecnologia (IIT), Genova, Italy

  2. These authors contributed equally: Dylan Murphy, Eralda Salataj.

Authors and Affiliations

  1. Sanford I. Weill Department of Medicine, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA

    Dylan Murphy, Eralda Salataj, Dafne Campigli Di Giammartino, Christopher M. Uyehara, Ly-sha Ee, UkJin Lee, Matthias Stadtfeld, Alexander Polyzos & Effie Apostolou

  2. Physiology, Biophysics and Systems Biology Program, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, USA

    Dylan Murphy

  3. Department of Pathology, New York University Langone Health, New York, NY, USA

    Javier Rodriguez-Hernaez, Andreas Kloetgen & Aristotelis Tsirigos

  4. Department of Medicine, New York University Langone Health, New York, NY, USA

    Javier Rodriguez-Hernaez, Andreas Kloetgen & Aristotelis Tsirigos

  5. Applied Bioinformatics Laboratory, New York University Langone Health, New York, NY, USA

    Javier Rodriguez-Hernaez, Andreas Kloetgen & Aristotelis Tsirigos

  6. Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Vidur Garg & Anna-Katerina Hadjantonakis

  7. Biochemistry Cell and Molecular Biology Program, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, USA

    Vidur Garg & UkJin Lee

  8. Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medical College, New York, NY, USA

    Erin Char

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Contributions

E.A. and A.P. conceived and designed the study and analyses with input from D.M., E.S., M.S., A.K.H. and A.T. All genomic and functional experiments were performed by D.M., E.S. and D.C.G. V.G. provided help with TSCs and XEN cell lines and L.E. provided material for the EpiSC genomics experiments. C.M.U. assisted with HiChIP visualization. U.L. assisted with CTCF ChIP-exo in ESCs. A.P. performed all computational analyses with help from J.R.H., A.K. and guidance from A.T. and E.A. E.C. performed all gene ontology analyses. E.A. wrote the manuscript together with D.M., E.S. and A.P. and input from all authors.

Corresponding authors

Correspondence to Aristotelis Tsirigos, Alexander Polyzos or Effie Apostolou.

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Nature Structural & Molecular Biology thanks Alvaro Rada-Iglesias, Pedro Rocha and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Tiago Faial, Carolina Perdigoto and Dimitris Typas were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Related to Figure 1.

a. Representative single (xy) stack epifluorescence images of immunofluorescence experiments showing expression of key lineage markers (greyscale) in TSC, ESC and XEN cells. Cells were counterstained with DAPI (blue) for DNA content. n = 3 independent experiments. Scale bar 100μm. b. Principal component analysis (PCA) of all TSC, ESC and XEN replicates based on their RNA-seq, ATAC-seq and H3K27ac ChIP-seq profiles. PCA plots were designed based on the top10% of most variable genes or peaks in all three cell lines. In each plot, circles indicate the experimental data presented in this study, while squares and triangles correspond to publicly available RNA-seq data (Supplementary Table 7) or independent -unpublished- studies from our lab, respectively. c. Stacked barplot showing the distribution of H3K27 occupancy among intergenic regions, gene bodies or TSS (promoter +/- 1.5 kb) for each K-Mean cluster as identified in Fig. 1c. Note: all statistics are provided in Supplementary Table 8.

Extended Data Fig. 2 Related to Figure 2.

a. Principal Component Analysis (PCA) plot of all lineages and replicates based on their compartment scores at 100 kb resolution (top) and on their TAD insulation levels at 40 kb resolution (bottom). b. Boxplots showing median expression changes between ESC and TSC (n = 4,327 genes), and TSC and XEN (n = 4156 genes) cells of genes in unaltered compartments (grey box and dashed line) or compartments with shifts as described in Fig. 2b. Asterisks indicate significance (p < 0.001) by two-sided Wilcoxon rank test. c. Volcano plot showing differential Hi-C interactivity at 40 kb resolution between ESC – TSC and TSC - XEN. X-axis shows delta interactivity while y-axis shows -log10(p-value) calculated by two-sided Student’s t-test. Significant changes (p-value < 0.05 and Diff>0.1 or <-0.1) are noted with blue and red color. d. Boxplots showing gene expression (n = 2352 genes) and enhancer strength (n = 15,982 peaks) changes between ESC-TSC regions with connectivity changes as described in Fig. 2c. Asterisks indicate significance (p < 0.001) by two-sided Student’s t-test. e. Boxplot comparing the sizes of HiChIP-detected loops in the three cell lineages across n = 2 independent Hi-C samples. f. Aggregate peak analysis (APA) showing the aggregate signal of MicroC data in ESC33 centered around ESC HiChIP interacting regions identified by FitHiChIP v9.0 at 5 kb resolution. (See Supplementary Table 4 and Methods). g. IGV tracks aligning H3K27ac HiChIP results (arcs on top and virtual 4C representation in the middle) with 4C-seq normalized signals around PDGFRA promoter in XEN along with corresponding H3K27ac ChIP-seq occupancy. h. Boxplot showing the median expression levels of a curated list of skipped and looped genes in ESC, XEN and TSC across n = 2 independent HiChIP and RNA-seq samples. Selected genes have similar ranges of H3K27ac signal at promoters. Asterisks indicate significance (p-value < 0.05), as calculated by two-sided Wilcoxon rank sum test. (See Supplementary Table 4). Note: all statistics are provided in Supplementary Table 8.

Extended Data Fig. 3 Related to Figure 3.

a. HiGlass visualization of H3K27ac HiChIP results around a TSC related hub (Cdx2) and a XEN-related hub (Gata6) in TSC, ESC, and XEN along with the corresponding H3K27ac HiChIP derived arcs and H3K27ac ChIP-seq signals. Interacting scores are presented in 5 kb resolution. b. Barplot showing the percentages of essential genes -as identified in two recent studies83,84- within the least (Q1) versus most (Q10) connected hubs. The preferential enrichment of essential genes in Q10 is significant (p-value < 0.001, two-sided Fisher’s exact test). c. Stacked barplots showing the proportions of different HiChIP loop subtypes in TSC, ESC and XEN cells. Loops were separated into 5 chromatin interaction categories based on the presence of regulatory elements, such as promoter/TSS (P) or putative enhancer (E, H3K27ac peak). X- anchors were defined as anchors that do not contain any TSS nor an H3K27ac peak. d. Boxplot showing the size distribution of X loops (X-E and X-P) compared to E-E, E-P and P-P loops in all cell lines. (n = 60,909 (TSC), 81,679 (ESC), 77,124 (TSC) loops across n = 2 independent HiChIP experiments). e. Boxplots showing expression changes between any two cell types around multiconnected genes (n > =5 in both cell types of interest), when at least one of their conserved anchors switches chromatin states: either from X-to-E (enhancer gain) or from E-to-X (enhancer loss) Asterisks indicate significance < 0.05 by two-sided Wilcoxon rank-sum test. (See also Supplementary Table 8). Note: all statistics are provided in Supplementary Table 8.

Extended Data Fig. 4 Related to Figure 4.

a. Correlation between differential HiChIP connectivity/hubness and differential gene expression in connectivity and differential gene expression in ESC and TSC cells (top) and TSC and XEN cells (bottom). R represents Spearman correlation identifies distinct groups of genes. We focus on the two most prominent groups: 3D-insensitive genes, defined as genes with differential connectivity >3 but no transcriptional changes (log2FC < 1 or >-1) and 3D-concordant genes for which connectivity and expression changes (log2FC > 1 or <-1) positively correlate (Supplementary Table 5). b-e. Gene ontology analysis depicting the most significant biological processes enriched in the 3D concordant and 3D-insensitive genes in each pairwise comparison (ESC vs TSC and TSC vs XEN) as defined in (a). All genes in A compartments were used as background. For further details see also Supplementary Table 3. f. Comparison of connectivity, gene expression levels as well as H3K27ac and ATAC CPM levels between ESC and TSC cells (left) and TSC and XEN cells (right) at promoters of 3D-concordant (n = 1818 for ESC/TSC and n = 1108 for TSC/XEN) and 3D-insensitive genes (n = 2637 for ESC/TSC, n = 2230 for TSC/XEN) as defined in (a). Insensitive genes show higher levels of connectivity, H3K27ac, ATAC and expression in both cell types. X-axis indicates cell type (T = TSC, E = ESC, X = XEN). Two-sided Wilcoxon rank sum test was used for all comparisons with p > 0.05 indicating significance. (Supplementary Table 8). Note: all statistics are provided in Supplementary Table 8.

Extended Data Fig. 5 Related to Figure 5.

a. Barplot of feature importance showing 10 1D (pink) and 3D (blue) features, ranked from high to low. Light blue indicates features not selected. (See Supplementary Table 6). b. Spearman correlation values for each variable considered for our 3D model with gene expression (left) and differential expression (right). Dots represent minimum, mean and maximum correlation scores. (See Supplementary Table 6). c. Area Under Curve (AUC) scores and Spearman Correlation for classifying gene expression (top 10% high vs low, left graph) and predicting levels (right graph) in ESC or TSC cells using 3D-HiChAT, Promoter and Linear models. Dots represent average scores from LOCO training approach (n = 20). Error bars show standard deviation. (See Extended Data Fig. 5, Supplementary Table 6). d. Plots showing AUC and Spearman correlation for classifying gene expression (top 10% high vs low, left graph) and predicting levels (right graph) using 3D-HiChAT in various lineages including mouse lineages and published human data: Naïve T cells, T-Helper 17 Cells (Th17), and T regulatory cells (Tregs)128,129. e. AUC scores and Spearman Correlation generated for classifying differential expression (top 10% up or downregulated, left) and predicting expression changes (right) between XEN and ESC using 3D-HiChAT, Promoter and Linear models. Dots represent average scores from LOCO training approach (n = 20). Error bars show standard deviation. (See Supplementary Table 6). f. Ranked perturbation scores (%) predicted by in silico perturbations of ~46 K E-P pairs in ESC, ~46.7 K in TSC and ~53.1 K in XEN using 3D-HiChAT. Dotted horizontal lines indicate selected cut-offs for impactful perturbations. g. Scatterplot comparing predicted perturbation scores from 3D-HiChAT with respective ABC scores. R Spearman correlation values are shown on the top. h. Boxplots showing that enhancers with high 3D-HiChAT-predicted perturbation scores and low ABC scores (red) are more distal to their target genes (loop size) than those with high scores in both models (blue) (left plot n = 3,428 enhancers). Enhancers/anchors with high 3D-HiChAT scores are more distal to the ones with high ABC scores (>0.7) (right plot n = 8,445 enhancers). Asterisks indicates significance calculated by two-sided Wilcoxon rank-sum test, p-val<0.001. Note: all statistics are provided in Supplementary Table 8.

Extended Data Fig. 6 Related to Figure 6.

a. Visualization of the Tfcp2l1 Locus showing H3K27ac HiChIP arcs, H3K27ac ChIP and Compartment c-scores called by Hi-C for TSC, ESC, and XEN. Notably, a group of putative enhancers upstream of Gli2 are uniquely expressed and only in an A compartment in ESC. b. IGV tracks of the Tfcp2l1-Gli2 locus showing the two enhancers chosen for functional validation, Enh3 and Enh14. H3K27ac HiChIP derived arcs originating from both enhancers are shown as well. RT-qPCR showing relative expression levels of Tfcp2l and Gli2 upon CRISPRi perturbation of Enh3 compared to control cells infected with empty vector (EV). Dots indicate independent experiments (n = 3). Error bars represent mean ± SD. Asterisks indicate significance, with p-value < 0.05, as calculated using unpaired one-tailed t-test. c. Schematic showing experimental strategy for generating a stable XEN line expressing dCas-BFP-KRAB (CRISPRi). Representative FACs plot from n > 12 independent experiments. d. AUC curve (red) showing a value of 0.71 when comparing our precited perturbation scores to our experimental validations presented in Fig. 6i for n = 40 different E-P pairs. e. Scatter plot comparing the predicted perturbation scores and the ABC scores for each of the 40 experimentally tested E-P pairs. Spearman Correlation value of -0.49. Different colors indicate different groups reflecting the concordance or discordance between predictions and experimental validations as shown in Fig. 6i. TP: true positive, TN: true negative, FP: false positive, FN: false negative. Note: all statistics are provided in Supplementary Table 8.

Supplementary information

Supplementary Information

Supplementary methods with the corresponding references

Reporting Summary

Supplementary Table 1

Genomic QCs

Supplementary Table 2

List of H3K27ac k-means peaks and super enhancers in each cell type

Supplementary Table 3

Summary of gene ontology and motif enrichment analyses

Supplementary Table 4

Summary of H3K27ac HiChIP contacts and hubs

Supplementary Table 5

List of 3D-concordant, discordant, and 3D-insensitive genes in each pairwise comparison and their corresponding gene ontology analysis.

Supplementary Table 6

Information about our 2D and 3D models: list of variables and models, performance and predictions from in silico perturbations.

Supplementary Table 7

List of oligonucleotides, reagents and resources

Supplementary Table 8

Statistical analyses for all figures

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Murphy, D., Salataj, E., Di Giammartino, D.C. et al. 3D Enhancer–promoter networks provide predictive features for gene expression and coregulation in early embryonic lineages. Nat Struct Mol Biol 31, 125–140 (2024). https://doi.org/10.1038/s41594-023-01130-4

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