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An atlas of transcribed human cardiac promoters and enhancers reveals an important role of regulatory elements in heart failure

Nature Cardiovascular Research volume 2, pages 58–75 (2023)Cite this article

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Abstract

A deeper knowledge of the dynamic transcriptional activity of promoters and enhancers is needed to improve mechanistic understanding of the pathogenesis of heart failure and heart diseases. In this study, we used cap analysis of gene expression (CAGE) to identify and quantify the activity of transcribed regulatory elements (TREs) in the four cardiac chambers of 21 healthy and ten failing adult human hearts. We identified 17,668 promoters and 14,920 enhancers associated with the expression of 14,519 genes. We showed how these regulatory elements are alternatively transcribed in different heart regions, in healthy versus failing hearts and in ischemic versus non-ischemic heart failure samples. Cardiac-disease-related single-nucleotide polymorphisms (SNPs) appeared to be enriched in TREs, potentially affecting the allele-specific transcription factor binding. To conclude, our open-source heart CAGE atlas will serve the cardiovascular community in improving the understanding of the role of the cardiac gene regulatory networks in cardiovascular disease and therapy.

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Fig. 1: Atlas of transcribed promoters and enhancers of healthy and failing human hearts.
Fig. 2: Differential expression analysis between healthy and failing hearts reveals chamber-specific differences.
Fig. 3: Differences in expression between healthy atria and ventricles.
Fig. 4: Failing atria exhibit two distinct expression programs.
Fig. 5: A comparison between NICM and ICM identifies disease-specific regulatory elements and pathways.
Fig. 6: SNP calling in CAGE data reveals functional variants in promoters.
Fig. 7: Proportions of GWAS SNPs in TREs.

Data availability

Heart CAGE data results were integrated using ZENBU (https://fantom.gsc.riken.jp/zenbu/reports/#Atlas%20of%20cardiac%20promoters%20and%20enhancers)116. Raw data are available on the NCBI GEO portal (GSE150736). All other data supporting the analyses presented in this study are provided in the manuscript and supplementary files.

Code availability

Unidirectional TRE annotation to genes was performed with https://github.com/Deviatiiarov/CAGE_peak_annotation. Differential expression analysis code can be accessed at https://github.com/Deviatiiarov/heart-CAGE-DE.

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Acknowledgements

This study was funded by the Leducq Foundation (RHYTHM to I.R.E. and A.G.), the National Institutes of Health (3OT2OD023848, R01HL126802 and U01HL141074 to I.R.E.), Russian Foundation for Basic Research grant 192904111 (to R. Syunyaev) and the University of La Verne Faculty Development fund (to T.V.T.). O.G. and I.V.K. were supported by the Ministry of Science and Higher Education of the Russian Federation (grant no. 075152021601).

Author information

Author notes

  1. These authors contributed equally: Ruslan M. Deviatiiarov, Anna Gams.

Authors and Affiliations

  1. Laboratory of Regulatory Genomics, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia

    Ruslan M. Deviatiiarov, Ivan V. Kulakovskiy, Andrey Buyan & Oleg Gusev

  2. Department of Biomedical Engineering, The George Washington University, Washington, DC, USA

    Anna Gams, Roman Syunyaev, Palak Shah & Igor R. Efimov

  3. Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia

    Ivan V. Kulakovskiy, Andrey Buyan & Georgy Meshcheryakov

  4. Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia

    Ivan V. Kulakovskiy & Tatiana V. Tatarinova

  5. I.M. Sechenov First Moscow State Medical University, Moscow, Russia

    Roman Syunyaev

  6. Inova Heart and Vascular Institute, Falls Church, VA, USA

    Ramesh Singh & Palak Shah

  7. Department of Biology, University of La Verne, La Verne, CA, USA

    Tatiana V. Tatarinova

  8. Graduate School of Medicine, Juntendo University, Tokyo, Japan

    Oleg Gusev

  9. RIKEN Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan

    Oleg Gusev

  10. Endocrinology Research Center, Moscow, Russia

    Oleg Gusev

  11. Department of Biomedical Engineering, Northwestern University, Chicago, IL, USA

    Igor R. Efimov

  12. Department of Medicine, Northwestern University, Chicago, IL, USA

    Igor R. Efimov

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  1. Ruslan M. Deviatiiarov
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Contributions

A.G., R. Singh, P.S. and I.R.E. collected samples. O.G. performed sequencing. R.M.D., A.G., I.V.K., A.B., G.M. and T.V.T. conducted data analysis. R.M.D., A.G., R. Syunyaev, T.V.T., O.G., I.V.K., A.B., G.M. and I.R.E. wrote and critically revised the manuscript.

Corresponding authors

Correspondence to Tatiana V. Tatarinova, Oleg Gusev or Igor R. Efimov.

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The authors declare no competing interests.

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Nature Cardiovascular Research thanks Vincent Christoffels, Albin Sandelin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Pipeline overview and general statistics of clustering and classifiers.

a. CAGE reads were aligned to human genome assembly v38 with BWA (unspliced) and unmapped reads were realigned with HISAT2 (spliced reads). Alignments were converted to CTSS (CAGE transcription start sites). b. CTSS were clustered using two methods: decomposition peak identification (DPI) and bidirectional enhancer pipelines to identify unidirectional and bidirectional TREs respectively. c. TREs were submitted to the TSSClassifier as 2 kb sequences: overlap with the EPD/FANTOM/ENCODE - training set, no overlap - test set. d. All TREs were connected to proximal GENCODE transcripts. Unidirectional TREs were associated with GENCODE genes if located within ±500-bp of the gene model start site. Bidirectional TREs were associated with genes if located in the same topologically associated domain (TAD) and supported by a correlation test FDR < 0.05 or located within the same TAD and supported by Expression Modifier Score (EMS) data. e. Classified TREs were extended into 400-bp regions. If 400-bp regions of the same class overlap, they were combined into one consensus region. For each gene, unidirectional TREs with the highest expression level represents the main TSS (highest CTSS peak position), and remaining TREs comprised an alternative TSS. For bidirectional TREs, the Mid position of the consensus region with the highest expression is the main Mid, and others are alternative Mid. f. External ChIP-seq data from Gilsbach et al. and ENCODE for ChIP-seq overlap-based classifier and ATAC-seq, DNase-seq, RAMPAGE, and RNA-seq data comparisons as supportive classifiers (Supplementary Table 2). g. Diagram explaining where each type of CAGE data was used. h. Trimming and mapping statistics. i. Genomic locations of all unidirectional TREs (top panel) and bidirectional TREs (bottom panel). j. Receiver operating characteristic (ROC) (left) and a number of classified peaks (right) of CAGE peak classification with a machine learning algorithm, TSSClassifier. The classifier was trained on regions from ENCODE (PLS - promoter-like sequences, pELS/dELS - proximal/distal enhancer-like sequences), FANTOM5 (refTSS), and EPD-eukaryotic promoter database). k. ChIP-seq based classification of TREs. l. TREs overlaps with databases of human heart (Supplementary Table 2). m. Bidirectional TRE overlaps with enhancer databases. n. Dinucleotide frequency in CTSS (highest peak position in unidirectional TREs).

Extended Data Fig. 2 Comparison of TREs of this study with other databases.

a. Upset plot highlights the intersection of TREs with different databases. To note, the FANTOM5 database includes the entire set of promoter peaks, while other resources are human heart-specific. b. TREs show higher specificity of epigenetic signals in comparison to other available databases of human heart promoters and enhancers. Data for ChIP-seq (H3K4me3, H3K27ac, PolR2A), ATAC-seq, and DNase-seq were obtained from ENCODE. The Y-axis shows the fraction of tags. CpG and phastCons values for 100-way genomic alignment were obtained from UCSC. The Y-axis shows fractions of CpG coverage or phastCons scores, respectively.

Extended Data Fig. 3 Comparison of failing heart differentially expressed genes defined by CAGE and ChIP-seq data.

a. Differential affinity analysis for Gilsbach et al. ChIP-seq active marks H3K4me3, H3K27ac. The number of defined differential TREs (FDR < 0.05) enriched in failing samples is much lower than in healthy samples, which is consistent with our observation of differential expression analysis (Fig. 2b,c). b. Differentially expressed TREs of failing samples have higher coverage by ChIP-seq active marks compared to healthy samples (likelihood ratio test, multiple testing corrected with Benjamini-Hochberg). Atrial and ventricular differentially expressed TREs have similar coverage. Example genes with consistent profiles in both CAGE and ChIP-seq data are shown as boxplots (CAGE healthy (n = 76) and failing (n = 33); H3K4me3 healthy (n = 3) and failing (n = 3); H3K27ac healthy (n = 2) and failing (n = 3)). The center line of the boxplot shows median, box bounds are first and third quartiles, whiskers indicate minima and maxima, and points show outliers. c. Coverage (CPM normalized) of CAGE TRE by ChIP-seq active marks (H3K4me3, H3K27ac) from Gilsbach et al. To note, there are two samples with a relatively low numbers of reads, SRR6426245 H3K27ac and SRR6426272 H3K4me3, and they appear as outliers on the heatmaps and principal component analysis plots on panel a. RLE - relative log expression, TPM - tags per million, CPM - counts per million.

Extended Data Fig. 4 Comparison of TREs with the FANTOM5 database.

a. Overlap of unidirectional TREs and bidirectional TREs with the FANTOM5 database. b. Aggregation plots for unidirectional TREs and bidirectional TREs using different epigenetic markers and scores.

Extended Data Fig. 5 Deconvolution analysis for heart CAGE data.

a. Single-cell RNA-seq data of adult human hearts were recalculated for cell deconvolution (www.heartcellatlas.org). b. Deconvolution results of heart CAGE data. Statistical differences between cell proportions in chambers were calculated using two-sided Student’s t-test with Bonferroni correction (n = 30 for LA, n = 29 for LV, n = 21 for RA, and n = 23 for RV samples). Only significant cases are labeled (adjusted p-value < 0.05). The center line of the boxplot shows median, box bounds are first and third quartiles, whiskers indicate minima and maxima, and points show outliers. LA - left atrium, LV - left ventricle, RA - right atrium, RV - right ventricle.

Extended Data Fig. 6 Differences in cell proportions between healthy and failing samples after deconvolution analysis.

a. Atrial CAGE libraries (n = 6 libraries for each chamber). b. Ventricular CAGE libraries (n = 76 for healthy and n = 33 for failing samples). Two-sided Student’s t-test with Bonferroni correction was applied. Only significant cases are labeled (adjusted p-value < 0.05). The center line of the boxplot shows median, box bounds are first and third quartiles, whiskers indicate minima and maxima, and points show outliers.

Extended Data Fig. 7 Specific GO terms for differentially expressed genes within selected groups.

Differentially expressed genes were defined as FDR < 0.05 and |log2(FC) | > 1 and submitted to the GO over-representation test. Significant GO terms (FDR < 0.05) were selected for each group, and log2(FDR) values were used for score calculation. Each cluster color (hierarchical clustering to the right of the heatmap) is numbered (number in a circle). Pathways corresponding to each cluster number are listed. Full clustering information (numbers in circles based on hierarchical clustering) is available in Source Data Extended Data Fig. 7 (column “cluster”).

Source data

Extended Data Fig. 8 KEGG pathway analysis of differentially expressed genes within selected groups.

KEGG enrichment analysis was applied to differentially expressed genes (similar to Extended Data Fig. 7), log2(FDR) was used for score calculation and hierarchical clustering. Each cluster color (hierarchical clustering to the right of the heatmap) is numbered (number in a circle). Pathways corresponding to each cluster number are listed. Detailed clustering data are available in Source Data Extended Data Fig. 8. KEGG - Kyoto Encyclopedia of Genes and Genomes, FDR- false discovery rate.

Source data

Extended Data Fig. 9 TFBS enrichment analysis for selected comparison groups.

Sequences of differentially expressed TREs were submitted to MEME Suite 5.3.3 AME with default parameters and Jaspar 2018 CORE non-redundant vertebrate motifs. Sequences of non-differentially expressed TREs were used as background. Bonferroni corrected p-values were transferred to log scale and used for score calculation and clustering. Each cluster color (hierarchical clustering to the right of the heatmap) is numbered (number in a circle). Motifs corresponding to each cluster number are listed. Clustering data are available in Source Data Extended Data Fig. 9. TFBS- transcription factor binding site.

Source data

Extended Data Fig. 10 Genomic variants associated with heart diseases and their potential to affect the regulatory network.

a. Enriched NHGRI-EBI GWAS SNPs (top) and TFBS (bottom) in differentially expressed CAGE TREs between healthy and failing heart samples. Numbers of TREs and representative examples are labeled. More detailed terms are available in Source Data Extended Data Fig. 10. b. An example of an SNP is rs186540595 (familial hypertrophic cardiomyopathy) which affects TFBS structure. Other cases are available on the Zenbu report platform. c. Visualization of rs186540595 SNP shows its proximity to differentially expressed CTSS of TNNI3.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–6

Reporting Summary

Supplementary Table 1

Donor demographics and sample statistics.

Supplementary Table 2

Libraries obtained from ENCODE and used in the analysis.

Supplementary Table 3

Libraries obtained from Gilsbach et al.23 and used in the analysis.

Source data

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Source Data Extended Data Fig. 7

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Source Data Extended Data Fig. 9

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Deviatiiarov, R.M., Gams, A., Kulakovskiy, I.V. et al. An atlas of transcribed human cardiac promoters and enhancers reveals an important role of regulatory elements in heart failure. Nat Cardiovasc Res 2, 58–75 (2023). https://doi.org/10.1038/s44161-022-00182-x

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