Transcriptionally active HERV-H retrotransposons demarcate topologically associating domains in human pluripotent stem cells

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Abstract

Chromatin architecture has been implicated in cell type-specific gene regulatory programs, yet how chromatin remodels during development remains to be fully elucidated. Here, by interrogating chromatin reorganization during human pluripotent stem cell (hPSC) differentiation, we discover a role for the primate-specific endogenous retrotransposon human endogenous retrovirus subfamily H (HERV-H) in creating topologically associating domains (TADs) in hPSCs. Deleting these HERV-H elements eliminates their corresponding TAD boundaries and reduces the transcription of upstream genes, while de novo insertion of HERV-H elements can introduce new TAD boundaries. The ability of HERV-H to create TAD boundaries depends on high transcription, as transcriptional repression of HERV-H elements prevents the formation of boundaries. This ability is not limited to hPSCs, as these actively transcribed HERV-H elements and their corresponding TAD boundaries also appear in pluripotent stem cells from other hominids but not in more distantly related species lacking HERV-H elements. Overall, our results provide direct evidence for retrotransposons in actively shaping cell type- and species-specific chromatin architecture.

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Fig. 1: Reorganization of TADs during human cardiomyocyte differentiation.
Fig. 2: Transcriptionally active HERV-H forms hESC-specific TAD boundaries.
Fig. 3: Deletion of two HERV-H sequences leads to the merging of TADs in hESCs.
Fig. 4: Silencing of HERV-H sequences weakens the TAD boundaries in hESCs.
Fig. 5: HERV-H insertion creates de novo TAD boundaries.
Fig. 6: HERV-H introduces new TAD boundaries during primate evolution.

Data availability

All sequencing datasets have been deposited in the Gene Expression Omnibus repository with the accession number GSE116862.

Code availability

Scripts are available at https://github.com/shawnzhangyx/cvdc_scripts.

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Acknowledgements

We thank S. Kuan and B. Li for sequencing and bioinformatic support. We thank E. Nostrand for RNA extraction. We thank M. Daadi (University of Texas Health Science Center at San Antonio) for providing marmoset iPSCs. We thank F. Gage (Salk Institute) for providing chimpanzee and bonobo iPSCs. This project is supported by funding from the Ludwig Institute for Cancer Research (to B.R.) and NIH (1UM1HL128773 to S.M.E., N.C.C., E.D. and B.R., and U54 DK107977 to B.R.). J.W. is the Virginia Murchison Linthicum Scholar in Medical Research. L.Y. was supported by a Hamon Center for Regenerative Science and Medicine fellowship from UT Southwestern Medical Center. S.P. was supported by a postdoctoral fellowship from the Deutsche Forschungsgemeinschaft (PR 1668/1-1). E.N.F. was supported by an NIH pre-doctoral training grant (5T32HL007444-35). M.L.A. was supported by an NIH training grant (T32GM008806-18). J.C.I.B. was supported by the Moxie Foundation.

Author information

N.C.C. and B.R. designed and supervised the experiments, analysis and data interpretation. Y.Z. implemented the analysis pipeline, analyzed all of the sequencing datasets, interpreted the results and designed the experiments for the HERV-H functional studies. T.L. generated the CRISPR–Cas9-edited cell lines for the HERV-H functional studies, and performed differentiation and quantitative PCR of the corresponding cell lines. S.P. performed the Hi-C experiments for all stages of the cardiomyocyte differentiation, and helped with interpretation of the results. M.L.A. analyzed the HERV-H knock-in data with help from Y.Q. regarding allelic analysis. J.D.G. and E.N.F. performed the cell culture and differentiation, and collected the cells for the Hi-C, ChIP-Seq and RNA-Seq assays. E.D. contributed to analysis and interpretation of the ChIP-Seq data. R.H. performed the Hi-C experiments for the HERV-H knockout, CRISPRi, HERV-H knock-in and primate iPSC cell lines. The ChIP-Seq experiments were performed by A.Y.L. (H3K27ac), S.C. (CTCF), and Q.Z. and H.H. (SMC3). Y.Q. and R.F. helped with the analysis of the Hi-C datasets. K.M. helped with the genome editing experiments. L.Y., J.C.I.B. and J.W. cultured and prepared the non-human primate iPSCs for sequencing and interpreted the data. Z.Y. performed the RNA-Seq experiments. S.M.E. helped with interpretation of the results. Y.Z., T.L., S.P., N.C.C. and B.R. wrote the manuscript with input from all authors.

Correspondence to Neil C. Chi or Bing Ren.

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Competing interests

B.R. is a co-founder of Arima Genomics.

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Integrated supplementary information

Supplementary Figure 1 Imaging and Flow cytometry data for cardiomyocyte differentiation.

(a) Immunostaining for MYL2 (white) shows exclusive expression in MYL2-H2B-GFP + hESC-derived cardiomyocytes after 80 days of cardiac differentiation. MYL2-H2B-GFP + cells were positive for cardiac troponin T (cTnT) (red), but not all cTnT+ cells were MYL2+ cardiomyocytes. DNA was stained with DAPI (blue). Images are representative of a minimum of three independent experiments. (b) Flow cytometric quantification of distinct markers for D2-D15 time points: T (D2), KDR/PDGFRα (D5), cTnT (D15). H2B-GFP + ventricular cardiomyocytes were sorted at day 80. Numbers represent the respective percentage of cells. The experiments were routinely carried out in Chi lab with similar results and had been repeated two times in this study.

Supplementary Figure 2 Quality metrics and reproducibility of sequencing datasets.

(a) Smoothed scatterplot of the Hi-C contacts between biological replicates for each stage. bin size = 100 kb. Spearman correlation coefficients are indicated on top of each sample pair. (b) Hierarchical clustering of Hi-C contact matrices based on 1-PCC (Spearman correlation coefficients) of the contacts between all samples, bin size = 100 kb. (c) Hierarchical clustering of the compartment A/B scores (PC1 values) between samples. (d) Hierarchical clustering of the insulation scores between samples. (e) Boxplot showing the number of reads for each sample. (f) Boxplot showing the percentage of alignments to human genome for each sample. (g) Boxplot showing the percentage of potential duplicated reads for each sample. (h) Boxplot showing the number of peaks called for each ChIP-seq sample. (e-h) For each boxplot the sample size N=12, the elements of the boxplot are: center line, median; box limit, upper and lower quartiles; whiskers, 1.5x interquartile range. (i) Barplots showing the relative expression levels (by RNA-seq) of representative genes during cardiomyocyte differentiation.

Supplementary Figure 3 Global changes in chromatin organization during cardiomyocyte differentiation.

(a) Frequency distribution of Hi-C contacts over genomic distances (log2) at different stages of cardiomyocyte differentiation (colored lines). (b) Barplot showing the percentages of compartment switches for each stage transition. (c) A histogram showing the distribution of the Pearson correlation coefficients (PCC) between gene expression levels and PC1 (indicating compartment A or B) value derived from the Hi-C contact matrix. (d) Snapshots of genome browser view of SOX2, HAND2 and RYR2 loci, showing PC1 values (Blue/red), RNA-seq (Black) and H3K27ac (Blue) signals.

Supplementary Figure 4 Dynamics of TADs during cardiomyocyte differentiation.

(a) Number of TADs in each stage of differentiation as defined by a domain caller algorithm first reported by Dixon et al. 20125. (b) Number of TADs (or loop domains) found by Arrowhead algorithm10. (c) Number of TADs found by Insulation score29. (d) Fraction of TAD boundaries that contain CTCF ChIP-seq peaks at each stage of cardiomyocyte differentiation. ‘stable’ group stands for TADs that are present at all stages. (e) Boxplot showing the dynamics of H3K27ac signal at ESC(+) TADs (N=198). (f) Boxplot showing the dynamics of RNA-seq signal at ESC(+) TADs (N=198). The elements of the boxplot are: center line, median; box limit, upper and lower quartiles; whiskers, 1.5x interquartile range.

Supplementary Figure 5 HERV-H elements are enriched at ESC(+) TAD boundaries.

(a) Aggregated RNA-seq expression profile (RPKM normalized) at ESC(+) TAD boundaries that overlap each of the eight repeat elements (HERVH-int and LTR7 are combined as they both belong to HERV-H). (b) Heatmap of DI scores within 70 kb of the top 50 most highly-expressed HERV-Hs. (c,d,e) heatmaps displaying Hi-C contacts for three ESC(+) TAD boundaries harboring HERV-H/LTR7 sequences are shown together with the DI, CTCF ChIP-seq, histone modification H3K27ac and RNA-seq profiles at the corresponding regions.

Supplementary Figure 6 Further characterization of the relationship between transcription levels at HERV-H elements and regular genes and formation of TAD boundaries.

(a) Aggregated DI score profiles of the TAD-boundary associated with HERV-Hs in multiple H1 ESC derived lineages and iPSCs. ESC: embryonic stem cell; MES: mesendoderm. MSC: mesenchymal stem cell; NPC: neural progenitor cell; TRO: trophoblast-like cell; iPSC: induced pluripotent stem cell. Interestingly, in human mesendoderm cells (an early human embryonic state that gives rise to mesoderm and endoderm cells), both the expression of these HERV-Hs and their corresponding TAD boundary strength were approximately half the levels compared to those observed in hESCs. There might be some un-differentiated cells in this population. (b) Heatmaps showing the aggregated DI score profile centered on the TSSs of genes (ranked by expression levels from high to low; every 1000 genes were segregated into bins, and separated by whether or not having a CTCF peak within 20kb of TSS). The DI score profile of top 50 HERV-H is shown at the bottom and the distribution of the HERV-H’s rank is shown on the right.

Supplementary Figure 7 Transcription of a solo LTR7 is correlated with appearance of TAD boundary in primate PSCs but not in mouse ESC.

(a) Hi-C interaction matrices of a solo LTR7 loci located at ESC(+) TAD boundaries at D0, D2 and D5 (top) are shown as heatmaps along with genome browser tracks of DI score, POLR2A, SMC3, CTCF, H3K27ac ChIP-seq and RNA-seq data of the expanded genomic region containing the TAD boundary (arrow). (b) Hi-C interaction matrices of the syntenic regions in bonobo iPSC, chimp iPSC and mouse ESC. (marmoset data not shown because the syntenic region is in an unassembled contig).

Supplementary Figure 8 Enrichment of TF or histone ChIP-seq signals at TAD associated HERV-H loci.

Barplot shows the ChIP-seq signal fold enrichment of the top 50 HERV-Hs comparing to HERV-Hs ranking 51-300. The red dashed line (value = 1) shows no fold enrichment.

Supplementary Figure 9 HERV-H knockout leads to alterations of gene expression programs in hESCs.

(a) Boxplots showing expression levels (RPKMs) of genes whose TSSs are located within TADs immediately 5’ (N=43) or 3’ (N=28) to boundary-associated HERV-Hs. P-values are from two-sided paired t-test on the log-transformed expression levels. The elements of the boxplot are: center line, median; box limit, upper and lower quartiles; whiskers, 1.5x interquartile range. (b) MA-plot (log ratio vs mean) showing average gene expression levels and fold changes of each gene in HERV-H1-KO and wild-type (WT). (c) Same as (b) but for HERV-H2-KO. (d) Scatterplot shows the changes in gene expression in HERV-H1-KO and HERV-H2-KO cells over WT cells. The red dots mark genes that with significantly changed gene expression in both mutant cell lines. The numbers of significantly changed genes in each Quadrant are indicated at the corner of each quadrant. Pearson correlation coefficient (PCC) and p-value are indicated (total number of genes N= 15623). (e) Barplot showing the number of significantly changed genes located within 20 kb of the HERV-H sequences. Genes down-regulated in both HERV-H knockouts were more likely to be within 20 kb of HERV-H sequences. P-value is from two-sided Fisher’s exact test (N=76). (f,g) RNA-seq profile of wild-type (WT) and HERV-H1-KO and HERV-H2-KO lines at the SCGB3A2 and LINC00458/HBL1 gene loci. The experiments were repeated twice independently with similar results.

Supplementary Figure 10 Analysis of chromatin contacts between actively transcribed HERV-Hs.

(a) Heatmap showing the contact matrix between the HERV-H1 locus and LINC00458/HBL1 locus. There is no visible chromatin contact between the two loci. (b) Heatmap shows the averaged and normalized (scaled and centered to zero) contact frequencies among each pair of HERV-H loci with RPKM greater than 1 (N=122). X-axis and y-axis show the genomic regions ±40kb surrounding HERV-H.

Supplementary Figure 11 Characterization of de novo HERV-H insertions in two engineered human ESC clones.

(a) Chromosomal view of de novo HERV-H insertions in the HERV-H-ins.clone1 transgenic line. The y-axis shows the counts of Hi-C read pairs with one end mapped to the HERV-H2 sequence. Based on proximity ligation principle, loci with high pileup should harbor HERV-H2 insertion. (b) Same as (a) but for the HERV-H-ins.clone2 transgenic line. (c) Genome browser view showing the Directionality Index (DI) score and transcription levels (RPKM) of the parental and HERV-H inserted cell lines. Note that in HERV-H-ins.clone1, the predicted HERV-H insertion creates a chimeric transcript with PIWIL1, which is not expressed in the other two cell lines. The experiments were repeated twice independently with similar results.

Supplementary Figure 12 Analysis of HERV-H related chromatin architecture and LTR7s in different primate PSC lines and in the mouse ESC.

(a) Heatmaps of DI scores within 70 kb of the syntenic regions of top 50 most highly-expressed human HERV-Hs in other indicated species. (b) Bar graph shows the percentage of HERV-Hs flanked by various types of LTRs. HERV-Hs are ranked by their expression levels in the hESCs, and grouped by bins of 50. (c) Violin plot shows the length of the flanking LTRs. HERV-Hs ranked and binned (N=50) as described in (b). The violin box displays kernel density of the LTR length distribution. (d) Boxplot shows the sequence divergence of the 5′ LTR and 3′ LTR for each bin of HERV-Hs. HERV-Hs ranked and binned (N=50) same as (b). The elements of the boxplot are: center line, median; box limit, upper and lower quartiles; whiskers, 1.5x interquartile range.

Supplementary Figure 13 A working model for HERV-H mediated TAD boundary formation.

When HERV-H is highly transcribed, the RNA polymerase complex moves directionally from the 5′LTR towards 3′LTR. Accumulation of the RNA polymerase complex presents a significant physical barrier to the movement of the cohesin complex (shown as a ring), causing it to accumulate at the 3′ end of HERV-H sequences, leading to creation of the TAD boundary. A previously characterized enhancer42 located at the 5′ LTR may regulate genes on its 5′ TAD, while the enhancer function is blocked by the TAD boundary formed downstream of the 3′ LTR.

Supplementary information

Supplementary Information

Supplementary Figs. 1–13

Reporting Summary

Supplementary Table 1

Summary statistics for the Hi-C data.

Supplementary Table 2

List of stage-specific TAD boundaries.

Supplementary Table 3

List of differentially expressed genes in HERV-H1-KO and HERV-H2-KO.

Supplementary Table 4

List of HERV-H insertions.

Supplementary Table 5

List of the primers and cell lines used in this study.

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