Cardiac cell type-specific gene regulatory programs and disease risk association

James D Hocker^{1

2

3}, Olivier B Poirion⁴, Fugui Zhu⁵, Justin Buchanan⁴, Kai Zhang³, Joshua Chiou^{2

6}, Tsui-Min Wang⁷, Qingquan Zhang⁵, Xiaomeng Hou⁴, Yang E Li³, Yanxiao Zhang³, Elie N Farah^{2

5}, Allen Wang⁴, Andrew D McCulloch^{7

8}, Kyle J Gaulton^{6

9}, Bing Ren^{10

4

9

11}, Neil C Chi^{12

11}, Sebastian Preissl¹³

Affiliations

¹ Medical Scientist Training Program, University of California, San Diego, La Jolla, CA, USA.
² Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.
³ Ludwig Institute for Cancer Research, La Jolla, CA, USA.
⁴ Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA.
⁵ Division of Cardiology, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
⁶ Department of Pediatrics, Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA.
⁷ Departments of Bioengineering and Medicine, University of California, San Diego, La Jolla, CA, USA.
⁸ Institute for Engineering in Medicine, University of California, San Diego, La Jolla, CA, USA.
⁹ Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA.
¹⁰ Ludwig Institute for Cancer Research, La Jolla, CA, USA. biren@health.ucsd.edu nchi@health.ucsd.edu spreissl@health.ucsd.edu.
¹¹ Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA.
¹² Division of Cardiology, Department of Medicine, University of California, San Diego, La Jolla, CA, USA. biren@health.ucsd.edu nchi@health.ucsd.edu spreissl@health.ucsd.edu.
¹³ Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA. biren@health.ucsd.edu nchi@health.ucsd.edu spreissl@health.ucsd.edu.

PMID: 33990324
PMCID: PMC8121433
DOI: 10.1126/sciadv.abf1444

Cardiac cell type-specific gene regulatory programs and disease risk association

James D Hocker et al. Sci Adv. 2021.

. 2021 May 14;7(20):eabf1444.

doi: 10.1126/sciadv.abf1444. Print 2021 May.

Authors

Affiliations

¹ Medical Scientist Training Program, University of California, San Diego, La Jolla, CA, USA.
² Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.
³ Ludwig Institute for Cancer Research, La Jolla, CA, USA.
⁴ Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA.
⁵ Division of Cardiology, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
⁶ Department of Pediatrics, Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA.
⁷ Departments of Bioengineering and Medicine, University of California, San Diego, La Jolla, CA, USA.
⁸ Institute for Engineering in Medicine, University of California, San Diego, La Jolla, CA, USA.
⁹ Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA.
¹⁰ Ludwig Institute for Cancer Research, La Jolla, CA, USA. biren@health.ucsd.edu nchi@health.ucsd.edu spreissl@health.ucsd.edu.
¹¹ Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA.
¹² Division of Cardiology, Department of Medicine, University of California, San Diego, La Jolla, CA, USA. biren@health.ucsd.edu nchi@health.ucsd.edu spreissl@health.ucsd.edu.
¹³ Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA. biren@health.ucsd.edu nchi@health.ucsd.edu spreissl@health.ucsd.edu.

PMID: 33990324
PMCID: PMC8121433
DOI: 10.1126/sciadv.abf1444

Abstract

Misregulated gene expression in human hearts can result in cardiovascular diseases that are leading causes of mortality worldwide. However, the limited information on the genomic location of candidate cis-regulatory elements (cCREs) such as enhancers and promoters in distinct cardiac cell types has restricted the understanding of these diseases. Here, we defined >287,000 cCREs in the four chambers of the human heart at single-cell resolution, which revealed cCREs and candidate transcription factors associated with cardiac cell types in a region-dependent manner and during heart failure. We further found cardiovascular disease-associated genetic variants enriched within these cCREs including 38 candidate causal atrial fibrillation variants localized to cardiomyocyte cCREs. Additional functional studies revealed that two of these variants affect a cCRE controlling KCNH2/HERG expression and action potential repolarization. Overall, this atlas of human cardiac cCREs provides the foundation for illuminating cell type-specific gene regulation in human hearts during health and disease.

Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

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Figures

**Fig. 1. Single-nucleus chromatin accessibility and transcriptome profiling of human hearts.**
(A) snATAC-seq and snRNA-seq were performed on nuclei isolated from cardiac chambers from four human donors without cardiovascular pathology. snATAC-seq: n = 4 (left ventricle), n = 4 (right ventricle), n = 3 (left atrium), and n = 2 (right atrium); snRNA-seq: n = 2 (left ventricle), n = 2 (right ventricle), n = 2 (left atrium), and n = 1 (right atrium). (B) Uniform manifold approximation and projection (UMAP) (*108*) and clustering analysis of snATAC-seq data reveal nine clusters. Each dot represents a nucleus colored by cluster identity. (C) UMAP (*108*) and clustering analysis of snRNA-seq data reveal 12 major clusters. Each dot represents a nucleus colored by cluster identity. Nerv., nervous. Art. sm. musc., arterial smooth muscle. (D) Genome browser tracks (*141*) of aggregate chromatin accessibility profiles [scale: reads per million (RPM)] at selected representative marker gene examples for individual clusters and for all nuclei pooled together into an aggregated heart dataset (top track, gray). Black genes below tracks represent the indicated marker genes, nonmarker genes are grayed. (E) Dot plot illustrating expression of representative marker gene examples in individual snRNA-seq clusters. (F) Heatmap illustrating the correlation between clusters defined by chromatin accessibility and transcriptomes. Pearson correlation coefficients were calculated between chromatin accessibility at cCREs within 2 kbp of annotated promoter regions (76) and expression of the corresponding genes for each cluster.

**Fig. 2. Characterization of gene regulatory programs in cardiac cell types.**
(A) Heatmap illustrating row-normalized chromatin accessibility values for the union of 287,415 cCREs. K-means clustering was performed to group cCREs on the basis of relative accessibility patterns. (B) Heatmap showing row-normalized chromatin accessibility of 19,447 cell type–specific cCREs (FDR < 0.01 after Benjamini-Hochberg correction; fold change > 1.2). K-means clustering was performed to group cCREs on the basis of relative accessibility patterns. Number of cCREs per K can be found in brackets. (C) Genomic Regions Enrichment of Annotations Tool (GREAT) ontology analysis (33) of cell type–specific cCREs. Q value for enrichment indicates Bonferroni-adjusted P value. NK, natural killer; GO, gene ontology; BP, blood pressure. (D) Transcription factor motif enrichment (35) for known and de novo motifs within cell type–specific cCREs. The heatmap in shows motifs with an enrichment P value of <10⁻⁵ in at least one cluster. For de novo transcription factor motifs, the best matches for the top motifs are displayed. Statistical test for motif enrichment: hypergeometric test. P values were not corrected for multiple testing. (E) Combination of transcription factor motif enrichment and gene expression shows cell type–specific roles for members of transcription factor families. Displayed are heatmaps for known motif enrichment in cell type–specific cCREs (left) and gene expression across clusters (right). (Fb., fibroblast; vCm., ventricular cardiomyocyte; aCm., atrial cardiomyocyte; Ec., endothelial; Sm., smooth muscle; Mac., macrophage; Lc., lymphocyte; Ad., adipocyte; Nr., nervous.)

**Fig. 3. Cardiomyocyte cCREs display chamber-dependent differences in chromatin accessibility.**
(A) Scheme for comparison of major cell types across heart chambers. (B) Volcano plot showing DA cCREs in each cell type between atria and ventricles. Each dot represents a cCRE, and the color indicates the cell type. cCREs with log₂(fold change) > 1 and FDR < 0.05 after Benjamini-Hochberg correction (outside the shaded area) were considered as DA. (C) Number of DA cCREs between atria and ventricles by cell type. (D) Heatmaps showing normalized gene expression levels of differentially expressed genes between atrial (aCM) and ventricular cardiomyocytes (vCM) that were linked by coaccessibility to distal DA cCREs that were more accessible in atrial cardiomyocytes (Atrial CM_DA) or ventricular cardiomyocytes (Ventr. CM_DA), respectively. (E) GREAT ontology analysis (33) of DA cCREs between atrial and ventricular cardiomyocytes. AV, atrioventricular. P values shown are Bonferroni adjusted (n.d., not detected). (F) Genome browser tracks (*141*) showing chromatin accessibility (scale, RPM) and gene expression [scale, reads per kilobase per million mapped reads (RPKM)] in atrial and ventricular cardiomyocytes as well as DA cCREs that were coaccessible with the promoter of *MYL2*. Gray dotted line indicates coaccessibility threshold (>0.1). Red shaded areas, distal DA cCREs coaccessible with *MYL2* promoter. Gray shaded areas, DA cCREs overlapping the promoter region of *MYL2.* (G) Transcription factor motif enrichment analysis (35) of DA cCREs between atrial and ventricular cardiomyocytes. The best matches for the top de novo motifs (score > 0.7) are shown. Statistical test for motif enrichment: hypergeometric test. P values were not corrected for multiple testing.

**Fig. 4. Cell type specificity of candidate enhancers associated with heart failure.**
(A) Cell type specificity of 4406 candidate enhancers with increased H3K27ac signal in failing left ventricles (15). Heatmap displays cell type–resolved chromatin accessibility RPKM values for cell types from left ventricular snATAC-seq datasets. Candidate enhancers were grouped on the basis of chromatin accessibility patterns across cell clusters using K-means. HF, heart failure. (B) Cell type specificity of 3101 candidate enhancers with decreased H3K27ac signal in failing left ventricles (15). (C) Genome browser tracks (*141*) showing several candidate enhancers with increased activity during heart failure that were primarily accessible in fibroblasts and coaccessible with the promoters of *LUM* and/or *DCN.* For visualization, linkages between cCREs within candidate enhancers and all gene promoters are shown (coaccessibility > 0.1, gray dotted line). Candidate enhancers coaccessible with gene promoters are indicated by red shaded boxes and promoter regions are indicated by gray shaded boxes (scale, RPM). (D) Genome browser tracks (*141*) showing several bulk candidate enhancers with decreased activity in heart failure that were primarily accessible in cardiomyocytes and coaccessible with the promoter of *IRX4* (scale, RPM). (E and F) Transcription factor motif enrichment (35) in the candidate enhancers with (E) increased and (F) decreased activity in failing left ventricles. Analysis was performed on the indicated K cluster(s) from (A) and (B), respectively. The best matches for selected de novo motifs (score > 0.7) are shown. Statistical test for motif enrichment: hypergeometric test. P values were not corrected for multiple testing.

**Fig. 5. Identification and characterization of AF-associated variants at the *KCNH2* locus.**
(A) Enrichment of cardiovascular disease variants within cardiac cell type cCREs. z scores are shown and were used to compute two-sided P values for enrichments. *FDR < 0.05 and ***FDR < 0.001. (B) Cardiomyocyte differentiation model schematic. (C) Fine mapping (55) and molecular characterization of two AF variants. Genome browser tracks (*141*) for snATAC-seq (top; scale, RPM) and indicated molecular features during hPSC-cardiomyocyte differentiation (scale, RPKM). Coaccessibility track shows linkages between the AF variant–containing cCRE and promoters (cutoff > 0.1). PPA, posterior probability of association (55). (D) Transgenic mouse embryo showing LacZ reporter expression under control of a genomic region [hs2192, VISTA database (58)] overlapping the variant-cCRE pair. (E) Luciferase reporter assay for the AF variant–harboring cCRE in D15 cardiomyocytes. Genotypes for rs7789146 and rs7789585 were either both G (risk), both A (nonrisk), or a combination. Each dot represents luciferase activity (average of two transfections) in independent replicates of D15 cardiomyocytes. Data are means ± SD. ***P < 0.001 and **P < 0.01 [one-way analysis of variance (ANOVA) and Tukey post hoc test]. Control: minimal promoter. (F) Expression of *KCNH2* and *TNNT2* in D25 cardiomyocytes after CRIPSR-Cas9–mediated cCRE deletion. Each dot represents an independent cardiomyocyte differentiation. Data are means ± SD. ***P < 0.001, **P < 0.01, and *P < 0.05 (one-way ANOVA and Tukey post hoc test); WT, unperturbed control. FC, fold change. (G) Action potential recordings in hPSC-derived cardiomyocytes with and without cCRE deletion at D25-35. (H) APD₉₀ (action potential duration at 90% depolarization) at 1-Hz pacing for four independent hPSC-derived cardiomyocytes with and without cCRE deletion at D25-35. Data are means ± SD. **P < 0.01 (unpaired two-sided t test).

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Cardiac cell type-specific gene regulatory programs and disease risk association

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Cardiac cell type-specific gene regulatory programs and disease risk association

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