Chamber-specific chromatin architecture guides functional interpretation of disease-associated Cis-regulatory elements in human cardiomyocytes

Domain: biology
Published: 2026-01-12
Editorial outcome: minor_revision

Abstract
Cis-regulatory elements (CREs) are noncoding DNA regions regulating cell-type-specific gene expression programs by interacting with distal gene promoters. Here, we aim to decode the function and spatial organization of CRE-promoter interactions in human cardiomyocytes. We analyzed the epigenome and chromatin interactions of human male atrial, ventricular, and failing cardiomyocytes. Atrial and ventricular cardiomyocytes harbored chamber-specific CRE-promoter interactions modulating gene expression as confirmed by functional epigenetic silencing. These CRE-promoter interactions explain the distinct contribution of non-coding genetic variants to atrial and ventricular diseases, such as dilated cardiomyopathy and arrhythmias. We dissected the prototypic KCNJ2 locus, encoding a potassium channel associated with ventricular arrhythmia susceptibility. Functional epigenetic silencing confirmed that CREs, harboring QT-duration-associated genetic risk factors, modulate KCNJ2 gene expression levels, alter KCNJ2-dependent channel currents, and affect cardiomyocyte repolarization. The presented human CM-specific chromatin interaction analysis provides key insights into regulatory mechanisms and aids in interpreting genetic risk factors.

Introduction
Cardiovascular diseases are among the leading causes of death in Western countries1. Heart diseases are driven by altered gene expression programs. Gene expression is controlled by distal cis-regulatory elements (CREs)2–5 in a time- and cell type-specific manner. The human genome harbors more than 1 million CREs4,5. These CREs are established and bound by transcription factors (TFs) and interact with target genes to modulate their expression6. CRE-target gene interactions can span large genomic distances but predominantly occur within topologically associating domains (TADs). TADs are regulatory domains formed through a loop extrusion process where ring-shaped cohesin complexes extrude a DNA loop until a boundary element is halted, often bound by the CCCTC-binding factor (CTCF)7. This dynamic process brings CREs and associated transcriptional regulators in frequent spatial proximity to their targets while insulating them from genes outside of the TAD. Sub-megabase-sized TADs form higher-order chromatin compartments, segregating the genome into active (A) and inactive (B) chromatin compartments8.

Epigenomic profiling methods allow annotation of putative CREs in an essentially unbiased manner. CREs are decorated by specific histone modifications (like H3K4me1), high chromatin accessibility, and low DNA methylation (LMR). These features are dynamically established during heart development and can be altered in disease9–15. The establishment of active CREs (enhancers) decorated by H3K27ac has been studied in fetal, adult, and diseased human hearts using cardiac tissue14,16,17 and human CMs11. A comparative analysis of mouse heart tissue and different cardiac cell types, including CMs, showed that CREs are established and respond to heart disease in a cell-type-specific manner18. A joint analysis of histone marks and DNA methylation in human CMs identified more than 100,000 CREs, many of which are enriched for non-coding variants associated with heart disease11,14,19. Thus, genetic variants within CREs may exert cell-specific effects; for example, ischemic heart diseases are likely linked to CRE variants in endothelial cells20, while cardiac arrhythmia and hypertrophy are likely associated with CRE variants in CMs. Such links may even differ between distinct CM populations (e.g., atrial, ventricular, or conduction system CMs)21.

Non-coding variants can modify TF binding to CREs, which can subsequently promote or disrupt the interaction dynamics of CREs with their target gene promoter, thereby leading to changes in gene expression22. Therefore, interpreting non-coding variants in the light of cell-type-specific CREs and chromatin interaction maps appears essential. High-throughput chromosome conformation capture sequencing (Hi-C) provides genome-wide resolution of chromatin interactions23, unlike targeted methods such as Hi-ChIP24 and Capture-Hi-C25 that rely on post-translational modifications or predefined probes. Notably, Hi-ChIP’s reliance on post-translational modifications limits its detection sensitivity26—unmodified regions are not assessed– and makes it challenging to distinguish true chromatin rewiring events from dynamic changes in chromatin states, thereby complicating mechanistic interpretation.

In this study, we aimed to resolve distal interactions of CREs with target promoters in human cardiomyocytes to link them to gene expression programs and genetic risk factors and to show their functional relevance through targeted functional perturbation. Therefore, we performed high-resolution Hi-C analysis of healthy atrial and ventricular CMs, failing ventricular CMs, and ventricular tissue to capture interactions independently of the epigenetic landscape. We integrated these data with epigenetic features and gene expression profiles. A comparative analysis demonstrated that CM-specific chromatin interaction data markedly outperform cardiac tissue-level data for sensitive detection of CM promoter interactions.

CM-specific data revealed distinct chromatin interaction landscapes between atrial and ventricular CMs. These chamber-specific interactions aligned with differential gene expression, including chamber-specific TFs, and were enriched for genetic risk variants associated with atrial and ventricular diseases, respectively. In ventricular CMs of failing hearts, promoter rewiring was linked to pathological gene expression of only eight genes and had no impact on genetic risk factor linkage. Using CRISPR interference (CRISPRi)-mediated perturbation27, we validated the functional relevance of several disease-associated chromatin interactions. Decoding of the distal regulatory landscape of the KCNJ2 locus identified regulatory elements controlling KCNJ2 expression, hence modulating KCNJ2-dependent repolarization currents.

Together, our findings advance the understanding of the regulatory architecture underlying CM function and support the interpretation of non-coding variants, providing a framework for prioritization of genome-wide association studies (GWAS) signals in human CM.

Results
A comparative analysis of chromatin interactions in human cardiac tissue and cardiomyocyte nuclei
CREs can be annotated using epigenetic signatures. Previous studies have shown that these signatures are highly cell-type-specific in the heart18,19,28. We therefore investigated whether CM-specific analysis of chromatin interactions could provide insights beyond those obtained from bulk cardiac tissue nuclei. To this end, we isolated bulk cardiac tissue nuclei or CM nuclei from three non-failing left ventricles (NF-LV) using fluorescence-activated nuclei sorting (FANS). Bulk tissue nuclei (NF-LV-tissue) were identified using the DNA dye Draq7, while CM nuclei (NF-LV-CM) were further specifically labeled with antibodies targeting pericentriolar material 1 (PCM1) and phospholamban (PLN)11,13,29 (Fig. 1a, b). Hi-C data of bulk cardiac nuclei and CM nuclei were generated from matched biological samples using the same methodology to avoid biases and minimize technical variability (Supplementary Fig. 1). We generated a chromatin state model for CM and cardiac tissue using published ChIP-seq data11 (Supplementary Fig. 2a) using ChromHMM11,30,31. Cis-regulatory elements (CREs) were identified as regions with low CpG methylation (LMRs)11,30,31 (Supplementary Fig. 2b). As an example, the locus containing the cardiac alpha-actin (ACTC1) gene is shown in Fig. 2a. The Hi-C contact map and virtual viewpoint analysis indicate lower interaction frequencies between the ACTC1 promoter and CREs in NF-LV-tissue compared to NF-LV-CM (Fig. 2a). We next expanded this analysis genome-wide. A comparative viewpoint analysis of more than one hundred CM marker genes32 revealed that CM-specific Hi-C data identify twice as many promoter-interacting domains (PIDs) in CM-specific data compared to tissue-level data, with a higher cumulative domain size (2.2 fold) and relative interaction frequency (1.98 fold) (Fig. 2b–d; Supplementary Figs. 3–7; Supplementary Data 1). In contrast, no significant differences were observed for housekeeping genes stably expressed across cardiac cell types18 (Fig. 2b–d) while several endothelial-, fibroblast-, and smooth muscle cell gene loci showed significantly higher interaction frequencies in NF-LV-tissue compared to NF-LV-CM (Supplementary Figs. 8–10). To determine whether these differences reflect changes in higher-order chromatin organization, we performed a principal component analysis (PCA) of the Hi-C contact matrices to infer A/B compartments. We identified 132 domains (covering 53.6 Mb, ~1.9% of autosomal genome) that switched compartment status between CM and tissue nuclei (Supplementary Fig. 11a). Notably, several CM-enriched genes including the muscarinic receptor M2 (CHRM2), sarcoglycan delta (SGCD), and the ryanodine receptor 2 (RYR2) resided in active (A) compartment in CM but inactive (B) compartment in bulk cardiac tissue nuclei (Supplementary Fig. 11b–d), highlighting the cell-type specific nature of higher-order chromatin structure.

Together, these results demonstrate that chromatin interactions and 3D genome organization in the human heart are highly cell-type-specific. CM-resolved analysis is therefore critical for accurately decoding the regulatory architecture of CMs.

Generation of human CM-specific Hi-C data for differential chromatin interaction analysis
The next aim was to generate CM-specific contact maps from a total of 23 biological replicates of non-failing left atria and left ventricle (NF-LA-CM, NF-LV-CM) as well as terminally failing ventricles (F-LV-CM; ejection fraction [EF] < 25%, Fig. 1c), for differential chromatin interaction analysis. In total, we generated Hi-C data from 9 NF-LV-CM, 6 NF-LA-CM, and 8 F-LV-CM samples with a total sequencing depth of 8.6 billion reads. Demographic characteristics were comparable across groups (Fig. 1c, Supplementary Data 2). Each biological replicate was sequenced to a depth of at least 200 million reads (Supplementary Fig. 1a, Supplementary Data 3). Quality control metrics demonstrated a high within-group correlation of chromatin interactions (Supplementary Fig. 1b), and the distribution of short- and long-range contacts was similar across all samples (Supplementary Fig. 1c).

Higher-order genome organization is conserved in human CM
We next asked whether higher-order chromatin organization, including TADs and A/B compartments, is different between the studied groups. Analysis of TADs revealed no significant differences among the different CM groups, since TAD insulation scores were highly correlated, and boundary strengths remained preserved across conditions (Supplementary Fig. 11e–f).

Similarly, principal component analysis of the Hi-C contact matrices revealed minimal differences in chromatin compartmentalization among groups. A comparison between F-LV-CM and NF-LV-CM identified only 18 genomic regions with A/B compartment switches (~3 Mb, ~0.1% of the autosomal genome) (Supplementary Fig. 11a). Notably, A/B compartments switches in failing CM were not associated with altered epigenetic marks or gene expression. Likewise, comparison of NF-LA-CM and NF-LV-CM identified only 7 switching A/B domains (~3.2 Mb, ~0.1% of the autosomal genome, Supplementary Fig. 11a), including loci harboring ACTA2 and PITX2.

Collectively, these results indicate that both TAD structure and A/B compartmentalization are highly conserved across CM subtypes despite associated transcriptional and epigenetic changes.

CM-specific promoter interaction analysis identifies spatially interacting CREs
Interactions of target gene promoters primarily occur within higher-order chromatin structures, such as TADs and A/B compartments. To resolve these interacting regions, we performed a genome-wide virtual viewpoint analysis for all coding-gene promoters. To characterize these interactions, we further integrated chromatin state annotations, DNA methylation profiles, and nuclear RNA-seq data, as shown in Fig. 1d for phospholamban (PLN). We used a DNA methylation-guided annotation for unbiased annotation of CREs across all groups (Supplementary Fig. 12). Consistent with established regulatory features, these CREs were enriched for H3K4me1, H3K27ac, and accessible chromatin, validating the annotation strategy6,30,31. Over 50% of annotated CREs overlapped with enhancer elements (H3K27ac+, H3K4me1+) and 20% with poised enhancer elements (H3K4me1+) (Supplementary Fig. 2c, d). In NF-LV-CM, we detected 357,918 PIDs spanning 54 Mb, with a median length of 7 kb (Supplementary Fig. 13a, b, Supplementary Data 4). A median number of 5 PIDs was detected per gene, located at a median distance of 147 kb from the promoter (Supplementary Fig. 13c, d). The majority of PI-CREs (77%) spanned one to multiple reference gene promoters (Supplementary Fig. 13e). PIDs overlapped 26 Mb of CRE sequence, and 9.8 Mb of ChromHMM-annotated33 enhancers (Supplementary Fig. 13a, f). Promoter-interacting CREs (PI-CRE) accounted for 36.8% of all annotated CREs and 41.5% of total CRE sequence length (Supplementary Fig. 13f). Based on ChromHMM classification, 54% of the promoter non-interacting CREs (PN-CRE) and 65% of the PI-CREs were classified as enhancers (Supplementary Fig. 13g). Notably, PI-CREs exhibited higher levels of H3K27ac marking compared to PN-CREs, suggesting enhanced regulatory activity (Supplementary Fig. 13h). Interestingly, across the NF-LV-CM, NF-LA-CM and F-LV-CM PIDs (Supplementary Data 4–6) we consistently identified between 55,000 and 59,000 PI-CREs per group (Supplementary Data 7–9).

CRISPRi-mediated silencing validates the functional relevance of PI-CREs
To assess the functional relevance of PI-CREs, we employed CRISPRi, which enables targeted epigenetic silencing without altering the genomic sequence. CRISPRi relies on the RNA-guided recruitment of a catalytically inactive Cas9 (dCas9) fused to a KRAB repressor domain to a specific genetic locus. CRISPRi vectors can be effectively delivered to human induced pluripotent stem cell-derived CMs (hiPSC-CM) using lentiviruses or adeno-associated viruses (AAVs) (CRISPRiLenti, CRISPRiAAV)27,34,35. We previously demonstrated that AAVi robustly represses target gene promoters in CMs, inducing local heterochromatin formation marked by specific H3K9me3 deposition and loss of chromatin accessibility34.

To evaluate the utility of CRISPRi for the functional interrogation of PI-CREs, we targeted several cardiac enhancers in ventricular hiPSC-CM (hiPSC-vCM), which were previously ablated. These experiments confirmed the functional link of two enhancers proximal to the MYL2 and MYH7 transcriptional start sites, which had previously been shown to control gene expression in mice and human embryonic stem cell-derived CM (ES-CM)9,36. We further validated an intronic KCNH2 enhancer reported by Hocker et al.19 as well as an SCN5A super-enhancer domain37 (Supplementary Figs. 14a–c, 15), both genetically linked to cardiac arrhythmias19,37. Silencing an alternative SCN5A super-enhancer domain did not impact SCN5A gene expression, consistent with prior genetic deletion experiments in mice37, reinforcing the validity of our CRISPRi approach and highlighting the functional conservation of enhancers across mammalian species.

Importantly, while genomic deletion of regulatory loci can lead to unintended chromatin rewiring38 and genomic rearrangements39, CRISPRi-mediated silencing does not modify the genome. Silencing of four loci did not impact the interaction strength with their linked promoter (Supplementary Fig. 14d–f). This suggests that the observed transcriptional effects are due to enhancer silencing rather than to disruption of 3D chromatin architecture. Together, these findings establish CRISPRi as a robust tool for the functional validation of PI-CREs in hiPSC-CM.

Chamber-specific promoter-CRE interactions are linked to gene expression
While atrial and ventricular CM share most of their gene expression repertoire, they differ in the relative abundance of transcripts40. To investigate whether these differences are associated with chamber-specific chromatin interactions, we analyzed Hi-C data of NF-LV-CM and NF-LA-CM (Supplementary Data 4 and 5).

Using a replicate-based analysis, we found 1,446 differential PIDs (Fig. 3a), of which 62% were characteristic of LA and 38% of LV. Chamber-specific PIDs were correlated with chamber-specific gene expression (Fig. 3b) and functional enrichment analysis linked them to pathways involved in CM differentiation and cardiac development processes (Fig. 3c). This includes ADRA1A, CCN2, HEY2, and TBX5; the latter two being key transcription factors known to drive chamber-specific gene expression41 (Fig. 4a, b, Supplementary Figs. 16, 17).

Next, we asked whether CRISPRi-mediated silencing of chamber-specific PI-CREs in hiPSC-vCM or hiPSC-aCM (atrial hiPSC-CM) can show their functional relevance. We first tested whether the hiPSC-aCM and hiPSC-vCM display the expected gene expression patterns using RNA-seq. These data confirmed that hiPSC-aCM and hiPSC-vCM expressed the expected marker genes and key chamber-specific genes studied in this project (Supplementary Fig. 18).

Silencing of PI-CREs linked to HEY2 and TBX5 significantly reduced the expression of their respective target genes, confirming the regulatory function of these elements (Fig. 4c, d).

We integrated data from the VISTA enhancer database42 to investigate whether chamber-specific gene expression can be driven by CREs independently of chromatin interactions. Of 380 enhancers shown to be active in the fetal mouse heart, 43% did not overlap with any PID in adult NF-LA-CM or NF-LV-CM (Fig. 3d). GREAT analysis43 revealed that these non-promoter-interacting VISTA enhancers were located within regulatory domains of genes involved in general and early development processes (Fig. 3e), suggesting that their activity is restricted to embryonic stages studied in the VISTA project or specific to non-CMs. In contrast, VISTA enhancers overlapping adult CM PIDs were linked to cardiac gene programs (Fig. 3e), with 14% residing within chamber-specific PIDs. A visual inspection of VISTA mouse embryos confirmed that many chamber-specific PIDs harbor enhancers with chamber-specific reporter gene activity (Fig. 3d). These findings demonstrate that PIDs drive chamber-specific gene expression programs, including key cardiac transcription factors.

Rewiring of the NPPA promoter
Hi-C viewpoint analysis revealed robust and dynamic interaction between the NPPA promoter and a ~20 kb stretch of clustered H3K27ac-marked enhancers (super-enhancer, Fig. 5a, yellow box). The interaction strength between the NPPA promoter and super-enhancer was significantly higher in NF-LA-CM compared to NF-LV-CM (Fig. 5a, b). Notably, CpG methylation levels at the super-enhancer and adjacent regulatory elements showed no significant differences, suggesting that interaction strength, rather than CpG methylation, underlies chamber-specific NPPA expression.

Because NPPA upregulation is a hallmark of heart disease and is associated with epigenetic changes11,16, we assessed chromatin rewiring at the NPPA locus in F-LV-CM. We identified PIDs in CM from failing hearts (F-LV-CM; Supplementary Data 6) and performed a replicate-based comparison to PIDs detected in NF-LV-CM. The NPPA promoter showed higher interaction frequencies with the super-enhancer region in F-LV-CM than in NF-LV-CM (Fig. 5a, c), mirroring the profile observed in NF-LA-CM. The increase in super-enhancer interactions was accompanied by enhanced NPPA expression and increased H3K27ac marking at the NPPA promoter, consistent with the transcriptional activation in failing CM. However, the histone modifications and CpG methylation at interacting CREs remained unchanged, suggesting that promoter-super enhancer rewiring underlies NPPA induction in failing CM (Fig. 5a).

To further dissect the structure-function relationship of the NPPA super-enhancer regulatory landscape, we performed a tiled viewpoint analysis across the super-enhancer region (Fig. 5d). In F-LV-CM, the NPPA promoter primarily interacted with the proximal part of the super-enhancer, whereas NF-LA-CM showed the strongest interaction with its distal part (Fig. 5d), suggesting that different enhancer segments mediate chamber-specific and disease-associated gene regulation.

We extended our analysis to the neighboring NPPB promoter, previously reported to interact with this super-enhancer, too ref. 44. In contrast to NPPA, the NPPB promoter specifically interacts with the central part of the super-enhancer in NF-LA-CM (Fig. 5e). Functional interrogation of this segment using AAV-CRISPRi (AAVi) in hiPSC-aCMs revealed that silencing the central super-enhancer segment reduced NPPB expression by 82% and NPPA expression by 31% (Fig. 5f, g).

These results indicate that distinct segments of the NPPA/NPPB super-enhancer orchestrate specific spatial and transcriptional control of gene expression in atrial and ventricular CMs, supporting a model in which distinct super-enhancer domains enable fine-tuned gene regulation across physiological and disease states.

Promoter rewiring in failing CM
Next, we conducted a genome-wide analysis of promoter rewiring in failing CMs and detected 4245 differential PIDs (Fig. 6a). Concordant changes in promoter interactions and gene expression (delta > 1.5 fold) were only detected for eight genes (ATOH8, NPPA, GNG8, NRG1, FMOD, DACT3, GMPR, and SORT1, Fig. 6a, b), including NPPA as described above (Fig. 5). A significant fraction of CREs losing promoter interaction strength in F-LV-CM contained a CTCF motif (Supplementary Fig. 19a). Integration of CTCF ChIP-seq data confirmed CTCF occupancy at these sites (Supplementary Fig. 19b). While CTCF is known to be implicated in TAD formation, TAD structure was preserved in failing CMs (Supplementary Fig. 11e, f). Differential PID interactions were constrained within TAD boundaries (Supplementary Fig. 19c), and only 2.9% of CTCF-marked differential PI-CREs were located at TAD boundaries.

We next asked whether chromatin interaction data link CRE activity to gene expression changes in failing CMs. Differential H3K27ac marking analysis identified two groups of CREs: group I (1848 CREs) with decreased H3K27ac and group II (1625 CREs) with increased H3K27ac levels (1626 CREs) in F-LV-CM (Fig. 6c). Linking these dynamic CREs to differentially expressed genes via PIDs, revealed that CREs gaining H3K27ac (group II) were associated with upregulated genes, whereas those losing H3K27ac (group I) were associated with downregulated genes in F-LV-CM (Fig. 6d). One representative example is HEY2 (group I). The distal CRE stably interacting with the HEY2 promoter in NF-LV-CM and F-LV-CM exhibited reduced H3K27ac signal and gene expression in F-LV-CM (Fig. 6e). Functional silencing of this CRE using CRISPRi in hiPSC-vCM recapitulated the downregulation observed in disease, confirming its regulatory role (Fig. 4d).

These results demonstrate that pathological gene expression changes rely primarily on preformed PIDs, and that pathological gene expression is linked to dynamic enhancer activity.

Regulatory mechanisms of cardiac disease-associated non-coding elements
Several studies have established that cardiac CREs are enriched for cardiac disease-associated genetic variants9,11,17,19,45–47. However, as in most cells of our body48, the target promoters of disease-associated CREs have not been systematically studied in a chamber- and CM-specific context. To address this, we analyzed the enrichment of disease-associated variants listed in the NHGRI-EBI GWAS catalog49 within promoter-interacting (PI-CREs) versus non-interacting CREs (PN-CREs, Supplementary Fig. 13a) as visualized in Fig. 7a.

Enrichment analysis for different disease traits revealed that only cardiovascular, heart, and respiratory disease-associated variants were significantly enriched in PI-CREs of NF-LV-CM compared to random variants (Fig. 7b). Further stratification of heart disease entities showed a stronger enrichment of cardiomyopathy-, QT interval-, and heart failure-associated variants in PI-CREs than PN-CREs of NF-LV-CM (Fig. 7c). The strongest genetic association of PI-CREs detected in NF-LV-CM was found for diseases that primarily affect the ventricles (Fig. 7c). In contrast, atrial fibrillation (AFIB)-associated variants displayed minimal enrichment in these NF-LV-CM PI-CREs. We then asked whether genetic variants associated with AFIB are enriched in PI-CREs of NF-LA-CM. Indeed, AFIB-associated variants were enriched in PI-CREs of NF-LA-CM (P = 5.9 × 10−22) as compared to NF-LV-CM (Fig. 7d, e). Similar trends were noted for heart disease and arrhythmia GWAS categories, largely reflecting the prevalence of AFIB-associated variants in them. In contrast, PI-CREs of NF-LV-CM carry more risk alleles for QT interval, cardiomyopathy, and heart failure (P = 1.3 × 10−46, 2.2 × 10−10, 1.6 × 10−5, respectively) as compared to PI-CREs detected in NF-LA-CM. Next, we confirmed the benefit of our chromatin interaction maps for disease variant interpretation using two recently published dilated cardiomyopathy (DCM)50 and QT51 GWAS studies. Consistent with our analysis of NHGRI-EBI GWAS catalog data49, NF-LV-PIDs were significantly enriched for DCM- and QT interval-associated variants (Fig. 7c-e). For this analysis, we selected variants meeting the criteria of GWAS (P < 5 × 10−8). To evaluate the potential of chamber-specific PI-CRE maps in annotating sub-threshold risk variants, we expanded this analysis to sub-threshold variants reported by Nauffal51 (P < 0.1) and performed an enrichment analysis (Fig. 7f). Strikingly, NF-LV-CM PI-CREs (Fig. 7f) were already strongly enriched for QT variants at significance levels of P < 10−5, while NF-LA-CM showed minimal enrichment independent of the chosen P value cut-off (Fig. 7f). CRE variants meeting GWAS or sub-threshold criteria were linked to genes of comparable gene ontology classes, including cardiac muscle cell membrane repolarization (GO:0099622). 86% of QT-associated CRE-promoter interactions spanned at least one other gene promoter, indicating that the nearest gene was often not the regulatory target. This underscores the importance of integrating chromatin interaction maps for ascribing biological function to non-coding genetic variants (Supplementary Fig. 13h). The sub-threshold cut-off criteria identified 25% more genetic variants of PI-CREs as compared to the GWAS criteria for this ontology term in NF-LV-CM (Fig. 7g). These findings demonstrate that integrating chamber-specific PI-CRE data enhance variant-to-gene mapping and supports the discovery of additional disease-associated regulatory loci.

Functional validation of disease-associated enhancers
To show the functional relevance of disease-associated PI-CREs we took advantage of published cardiac “expression quantitative trait loci” (eQTL) data52. Chamber-specific PIDs linked genetic variation to gene expression changes in human hearts, supporting the functional relevance of PI-CREs. This included genetic variants spatially interacting with AFIB and long QT syndrome-associated genes such as KCNK3 and NOS1AP53,54 (Fig. 7h–j). We next aimed to test the functional relevance of individual disease-associated PID-CREs using CRISPRi in hiPSC-CM. We selected several CREs genetically linked to QT duration due to the strong enrichment of this trait in PI-CREs of NF-LV-CM (Fig. 7c–f). CRISPRi-mediated silencing of two QT-linked CREs reduced gene expression of their respective target genes KCNH2 and SCN5A in hiPSC-vCM (Supplementary Fig. 14a and Supplementary Fig. 15), consistent with previous reports19,37.

We next aimed to decode the regulatory landscape of KCNJ2, since it harbors several uncharacterized CREs carrying QT variants, including CREs overlapping with variants meeting genome-wide significance (P < 5 × 10−8) and sub-threshold (P < 10−5) variants. We restricted this analysis to active CREs marked by H3K27ac in LV-CM (Fig. 8a, Supplementary Fig. 20). KCNJ2 encodes the inward-rectifier potassium channel Kir2.1, a key contributor to the IK1 current. We first tested whether our matured hiPSC-vCM expressed functional levels of KCNJ2. KCNJ2 transcript levels were higher in hiPSC-vCM than in hiPSC-aCM but remained lower than in native LV tissue (Supplementary Fig. 21a), consistent with previous reports55,56. Automated high-throughput patch-clamp experiments detected Kir2.1 currents, reflecting functional KCNJ2 channels, in 72.5% of hiPSC-vCM (Supplementary Fig. 21b–d). This aligns with our previous observations57 and confirms the suitability of hiPSC-vCM for functional studies. Using CRISPRi, we systematically silenced six KCNJ2 interacting domains (regions 3–8), one non-interacting (region 2), and the KCNJ2 promoter (region 1, positive control) in hiPSC-vCM. Silencing of the KCNJ2 promoter led to near-complete (98.4%) loss of gene expression (Fig. 8b), while silencing of the non-interacting region 2 or the interacting region 8 did not significantly impact KCNJ2 expression. The silencing of interacting regions 3, 4, and 6 resulted in 39.7%, 65.4%, and 50.5% reduction of KCNJ2 gene expression, respectively (Fig. 8b), confirming their enhancer activity. Two independent genetic deletions of region 4 validated these findings (Supplementary Fig. 22). Combinatorial silencing of adjacent regions 3 and 4 (Fig. 8c) did not find a significant additive effect. Chromatin accessibility analysis (ATAC-seq) for their individual and combinatorial silencing (Fig. 8d) demonstrated the specificity and efficacy of CRISPRi-mediated silencing. Chromatin interaction analysis demonstrated that CRISPRi-mediated silencing did not affect the interaction strength of regions 3 and 4 with the KCNJ2 promoter (Supplementary Fig. 14f). Given that regions 3 and 4 are enriched in disease risk variants and critical for KCNJ2 transcription, we hypothesized that their modulation, by affecting the level of functional KCNJ2, would also impact the inward-rectifier IK1 currents. Silencing of the KCNJ2 promoter resulted in a 90% reduction of IK1 currents, whereas silencing of regions 3 and 4 reduced IK1 currents by 39% and 67%, respectively, closely mirroring the transcriptional effects (Fig. 8e, Supplementary Fig. 21b–f). Multi-electrode arrays (MEA) recordings of field potentials further demonstrated that silencing these regions in hiPSC-vCMs delayed repolarization time, mimicking QT interval prolongation (Fig. 8f).

Collectively, the dissection of the KCNJ2 locus identified key enhancer elements modulating its expression levels and the repolarization kinetics in CMs, providing mechanistic insights into the genetic basis of QT syndrome. Notably, the functionally relevant region 3 does not overlap with QT duration-associated GWAS variants but instead harbors a sub-threshold variant (Fig. 8a).

Finally, we note that the respective promoter interactions are barely detectable in LV-tissue Hi-C data (Supplementary Fig. 4), emphasizing the benefit of cell-type-specific epigenome and chromatin-interaction profiling, combined with functional modulation, to decode the disease relevance of non-coding regulatory elements.

Methods
Human cardiac biopsies
Cardiac biopsies from male hearts were used to isolate CM nuclei. These investigations were approved by the ethics committees of the Universities of Heidelberg, Munich, and Freiburg (Germany). Informed consent of human participants was obtained for surgical biopsies. Informed consent was waived by the ethical committee for samples from accidental deaths. Samples were not used if a participant or next of kin dissented from the scientific use. The study complies with the Declaration of Helsinki. All samples retrieved during interventions were immediately flash-frozen and stored at −40 to −80°C. Tissue from accidental death was flash-frozen during the forensic autopsy. To comply with ethics committee approval requirements and protect patient data security, we removed data that could identify patients, including patient-specific sequence variation. Measurements of each biological replicate were taken from distinct samples listed in Supplementary Data 2. All tissue samples were fully used for experimental analysis.

FANS of cardiomyocyte nuclei
All steps were performed at 4–8°C to ensure nuclei integrity13. Frozen tissue (<100 mg) was thawed in 3 mL lysis buffer (5 mM CaCl2, 3 mM MgAc, 2 mM EDTA, 0.5 mM EGTA, 10 mM Tris-HCl, pH 8) and homogenized using a Miltenyi gentleMACS dissociator equipped with M tubes using the “protein_01” protocol. 3 mL of lysis buffer with 0.4% Triton X-100 was added. The suspension was filtered through a 40 μm cell strainer (BD Bioscience), and the filter was washed with 2 mL lysis buffer. The suspension was centrifuged (1000 × g, 5 min, 4°C), and the pellet was resuspended in 1 mL lysis buffer. The suspension was overlayed onto a 1 M sucrose cushion (3 mM MgAc, 10 mM Tris-HCl, pH 8) and centrifuged (1000 × g, 5 min, 4°C). The pellet was resuspended in 500 μl staining buffer (PBS, 1 % BSA, 22.5 mg/mL glycine, and 0.1% Tween 20) containing rabbit anti-PCM-1 antibody (1:1000, HPA023374, Sigma-Aldrich) and mouse anti-PLN antibody (1:1000, A010-14, Badrilla). After 30 min incubation at 4–8°C, anti-rabbit secondary antibody conjugated to Alexa 568 (1:1000, A11011, Life Technologies) and anti-mouse secondary antibody conjugated to Alexa 488 (1:1000, A11029, Life Technologies) were added and incubated for an additional 30 min. After centrifugation (1000 × g, 5 min, 4°C), the nuclei were resuspended in PBS buffer with Draq-7 (1:100, Cell Signalling Technology). CM nuclei (PCM1+, PLN+, Draq-7+) were sorted using a SH800S cell sorter (Sony Biotechnology). FSC pulse width was used to exclude doublets from sorting, and PCM1- and PLN-double positive nuclei were selected. Sorted CM nuclei were processed immediately for downstream applications.

Methyl-seq
Genomic DNA was extracted from the FANS-derived CM nuclei using AllPrep DNA/RNA Micro Kit (Qiagen, 80284). 200 ng of isolated DNA was sheared to ~300 bp using a Bioruptor Pico (Diagenode, 10 cycles, 30 s on, 90 s off, “low”). Sequencing libraries were subsequently constructed using the NEBNext Methyl-seq Kit (New England Biolabs, E7120), following the Protocol for Standard Insert Libraries.

Hi-C
At least 25,000 sorted CM nuclei (up to 225,000 nuclei per technical replicate) or 20,000–50,000 hiPSC-vCMs were used for in situ Hi-C using the Arima Genome-wide HiC Kit and processed according to the manufacturer’s documentation. DNA fragmentation was performed with a Bioruptor (Diagenode, 30 cycles, 30 s on, 90 s off, ‘low’) to obtain an average fragment size of 400 bp. The final Hi-C library was amplified using the KAPA-SYBR Fast DNA amplification reagents (Kapa Biosystems) supplemented with EvaGreen (Biotium) in technical replicates. The final sequencing libraries were constructed using the Swift Bioscience indexing kit according to instructions provided by Arima. Technical replicates were pooled and cleaned-up twice using 0,9x Ampure Beads XP (Beckman) to remove contaminating adapter and primer dimers.

ChIP-seq
A minimum of 5000 sorted CM nuclei were used for ChIP-seq according to the manufacturer’s protocol (Low Cell ChIP Kit, Active motif, 53086) using an antibody targeting H3K27ac (Active motif, 39133). Libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB, E7645) according to manufacturer’s instructions. We monitored library amplification upon addition of EvaGreen using a qPCR cycler (Agilent). Amplification was stopped when the fluorescent signal reached the inflection point.

hiPSC differentiation into atrial and ventricular cardiomyocytes
Three hiPSC lines were used in this study: Crt173, LiPSC-GR1.174 (kindly provided by W.-H. Zimmermann, IPT/UMG) and HipSci HPSI1013i-wuye_375 (ECACC 77650131, generated using the CytoTune®-iPS Kit from Life Technologies and kindly provided by the Wellcome Trust Sanger Institute, UK). hiPSCs were maintained in TeSR-E8 medium (Stem cell, 05990) on Geltrex-coated cell culture dishes prior to differentiation. CM differentiation was induced by the temporal modulation of the Wnt pathway76. Wnt activation was initiated on day 0 using CHIR99021 (Hölzel, C-6556). After 48 h, Wnt inhibition was performed by treating cells with IWP2 (Sigma-Aldrich, 681671) for 48 h. For atrial cardiomyocyte specification, cells were stimulated with retinoic acid (Sigma-Aldrich, R2625) between day 3 and day 6 of differentiation77,78. A high-purity CM population was achieved using the metabolic selection method79. The cellular identity was regularly confirmed based on cell morphology and beating frequency77 as well as using flow cytometry (Sony SH800). Staining of hiPSC-aCM and hiPSC-vCM using anti-cTNT antibodies (Miltenyi, REA400|1C11) revealed >90% of cTNT-positive CM after metabolic selection as previously described77. In case of atrial cardiomyocyte differentiation, ~90% of cells were identified as atrial CMs using antibodies targeting MLC2a (Miltenyi, REA398|56F5). In case of hiPSC-vCM about 90% of cells were marked by an antibody recognizing the ventricular CM marker MLC2v (Miltenyi, REA401|330G5)77. Representative QC data is shown in Supplementary Fig. 18a–d.

Generation of enhancer knockout hiPSC cell lines
The ribonucleoprotein complex (RNP) was assembled by incubating ArciTect Cas9-eGFP Nuclease (Stem Cell Technologies, 76006) with custom-designed synthetic sgRNA (Genscript, listed in Supplementary Data 10) for 15 min at room temperature, following the manufacturer’s protocol. Alt-R CRISPR-Cas9 Electroporation Enhancer (IDT, 1075916) was then added to the RNP complex.
hiPSCs were dissociated into a single-cell suspension using Accutase. Transfections were performed using the Neon transfection system (Thermo Fisher Scientific). A 5×105 hiPSCs cell pellet was resuspended in 15 µl Neon Buffer R and mixed with 15 µl RNP complex. Electroporation was carried out with two pulses at 1100 V for 30 ms. Cells were then plated onto Matrigel–coated plates and cultured in mTeSR Plus medium (Stem Cell Technologies, 100-0276) supplemented with CloneR2 (Stem Cell Technologies, 100-0691). Transfected cells were grown for 24–36 h.
Cells were then harvested using Accutase, resuspended in mTeSR Plus medium supplemented with CloneR2, and stained with 7-Aminoactinomycin D (7AAD) (VWR, CAYM11397) to assess viability. The cell suspension was filtered through 30 µM CellTrics filters (Sysmex, 04-004-2326). 7AAD-negative/GFP-positive single cells using a SH800S cell sorter (Sony Biotechnology) equipped with a 130-µm nozzle. Forward scatter width and height parameters were used to exclude doublets. Single cells were sorted into Matrigel–coated 96-well plates containing mTeSR Plus medium supplemented with CloneR2. After recovery and clonal expansion, genotyping of the clones was performed using Phire Tissue Direct PCR Master Mix kit (Thermo Scientific, F170). Primers used for PCR screening are listed in Supplementary Data 10.

sgRNA cloning into lentiviral CRISPRi vector and lentivirus production
An expression plasmid was generated using a plasmid obtained as a gift from Charles Gersbach35 (Addgene #71237, pLVhU6-sgRNA-CAG-dCas9-KRAB-T2a-GFP) in which a synthetically synthesized DNA fragment replaced the human U6 and ubiquitin C promoter. This 1105 bp DNA fragment consisted of the following components: Mouse U6 promoter, gRNA entry site, and CMV enhancer. For this exchange, we used the restriction enzymes PacI and XbaI. To clone sgRNAs (Supplementary Data 10) into the expression plasmid, two complementary oligonucleotides (ordered from eurofins genomics) were annealed and ligated into the expression vector via Golden Gate cloning with Esp3I. The ligation product was transformed into competent Escherichia coli cells, and plasmids of positive clones were isolated using the NucleoSpin Plasmid Mini kit (Macherey-Nagel). CRISPRi expression vectors containing sgRNAs, specific for one regulatory element, were pooled equimolarly. Together with envelope and helper plasmids (gifts from Didier Trono, Addgene: 12259, 12260), and were used for lentivirus production in HEK293T cells.

sgRNA cloning into AAVi vector and AAV6 production
sgRNAs (listed in Supplementary Data 10) were cloned into AAVi plasmid encoding Staphylococcus aureus dCas9-KRAB (Addgene #214609)34 using golden gate cloning with BbsI (NEB, R3539). The ligation product was then transformed into chemically competent Escherichia coli cells, and plasmids of positive clones were isolated using the NucleoSpin Plasmid Mini kit (Macherey-Nagel).
AAV6 production was carried out in AAVpro 293T cells (Takara, 632273), which were co-transfected with AAVi plasmid and AAV helper and packaging plasmid (rep2/cap6) using polyethylenimine (Sigma-Aldrich, 408727). Viral particles were purified using AAVpro extraction solution (Takara, 6235) following the manufacturer’s protocol. The purified AAV particles were concentrated using 100-kDa Amicon Ultra Centrifugal Filter Units (Sigma-Aldrich, UFC510096). The AAV titer was determined by qPCR using the AAVpro Titration Kit v2 (Takara, 6233).

Transduction of hiPSC-CMs
For all experiments we used at least three independent hiPSC-CM differentiations. AAV- or lentiviral-mediated transductions were performed on hiPSC-CMs at post-differentiation day > 50.
Lentiviruses were used to transduce hiPSC-CMs at a multiplicity of infection (MOI) of 3, facilitated by the addition of polybrene (Merck). GFP-positive lenti-transduced hiPSC-CMs were sorted into RLT buffer (Qiagen) after 12 days for gene expression analysis or into PBS containing 1% BSA for ATAC-seq and Hi-C analysis. In case of AAVi, transduction was performed at an MOI of 5000–30,000, and cells were collected after 7 days for subsequent analysis.

RT-qPCR and RNA-seq
RNA was isolated with the RNeasy Micro Kit (Qiagen), including on-column DNase digestion. For RT-qPCR analysis RNA was transcribed into cDNA using the SuperScript™ IV First-Strand Synthesis System (Invitrogen, 18091300), and the 1x SsoAdvanced Universal SYBR® Green Supermix (Bio-Rad, 1725275) was used to determine the expression of the target genes using a Roche LightCycler® 480 Instrument II using primers ordered from eurofins genomics. The SMART-Seq Total RNA Pico Input (ZapR Mammalian, Takara, 634356) was used for the generation of RNA-seq libraries.

Automated patch-clamp recordings
Inward-rectifier K+ currents were recorded using SyncroPatch 384 (Nanion Technologies GmbH, Germany), as described previously80,81. hiPSC-vCMs were analyzed on thin borosilicate glass, single-aperture 384-well planar fixed-well APC chips (1xS-type NPC-384T). Inward rectifier potassium currents (IK1) were measured by applying a ramp pulse from −100 to +40 mV (0.5 Hz).
The external KCl concentration of 20 mM was used to facilitate a positive shift in IK1 reversal potential, which allows for a larger current acquisition of the inward component of IK1 at −100 mV. IK1 was identified as current responsive to Ba2+ blockade (1 mM).

Multi-electrode array (MEA) measurements
hiPSC-vCM were seeded at a density of 30,000 cells in 5 µl media onto Geltrex-coated recording electrodes of a CytoView MEA plate (Axion Biosystems) using the spot seeding method with PBS in the space between the wells to create a humid atmosphere preventing cells from drying out in the process. Six hours after seeding, 300 µl media was added to each well. Baseline field potential duration was recorded 2 days post-seeding using a Maestro detection system (Axion Biosystems). Cells were equilibrated for at least 30 min at 37°C and 5% CO2 within the system prior to recording. Following baseline measurements, cells were transduced with AAV viral particles at MOI > 30,000 Vg/cell. Field potentials were recorded 7 days post-transduction. The AxIS Navigator (version 3.12.7) and the Cardiac Analysis Tool (3.3.3) were used to record and analyze the cardiac activity parameters of spontaneous action potentials.

ATAC-Seq
For ATAC-seq we used the OMNI-ATAC protocol82. The transposition approach was scaled to cell/nuclei number (2.5 µL Tn5 Tagment DNA Enzyme 1 (Illumina) per 50,000 cells). Samples were amplified after the addition of EvaGreen, and the fluorescence was monitored during the amplification. Amplification was stopped when the fluorescent signal reached the inflection point.

Sequencing of DNA libraries
The concentration of DNA libraries was determined by Qubit (Invitrogen), and the insert size using a Bioanalyzer or Tapestation (Agilent Technologies). Pooling of multiplexed sequencing samples, clustering, and sequencing were carried out on Nextseq 2000, Novaseq (Illumina) or AVITI (Element Biosciences) sequencers. All libraries were sequenced in paired-end mode.

Methyl-seq analysis
Raw reads were trimmed as paired-end reads using TrimGalore! (https://github.com/FelixKrueger/TrimGalore) with default parameters. Trimmed reads were aligned to human genome assembly hg19 using bwameth (Galaxy Version 0.2.2+galaxy1, paired-end mapping) and PCR duplicates removed using RmDup (Galaxy Version 2.0.1). CpG-methylation metrics were extracted using MethylDackel (Galaxy Version 0.3.0). For visualization of CpG methylation, values were smoothed using a centered running window of 3 CpGs.
MethylSeekR31 was used to partition the genome. Partially methylated domains (PMDs) were identified after training on chromosome 1, and PMDs shorter than 100 kb were filtered out. Subsequently, low methylated regions (LMRs) and unmethylated regions (UMRs) were annotated using the parameters m.sel=0.5 and fdr.cut-off=10. Regions overlapping annotated transcription start sites were filtered since they are likely linked to gene body demethylation and do permit CRE annotation11,83. A replicate-based differential methylation analysis of CREs was conducted using Metilene84. A minimum mean methylation difference of 10% and an adjusted p value (q < 0.05) were considered significant.

RNA-seq data analysis
Published gene expression data sets were mapped to the human genome (hg19) using RNAStar85 and PCR duplicates were removed using RmDup (Galaxy Version 2.0.1). Cufflinks86 was used to calculate FPKM expression values. Reads were counted and Deseq287 was used to normalize count tables and identify differentially expressed genes using an FDR corrected p value of 0.05.

ATAC-Seq analysis
ATAC-Seq sequencing data were cleared of adapter sequences using Cutadapt88. We used Bowtie289 to assign the sequencing data to the corresponding position in the human genome hg19 in paired-end mode. The maximum fragment length was set to 1000 bp. Sequencing pairs pointing away from each other were allowed (--dovetail yes), and the sensitive (--very-sensitive) mode was used. PCR duplicates were removed using RmDup (Galaxy Version 2.0.1).

ChIP-seq analysis
We used Bowtie289 to assign the sequencing data to the corresponding position in the human genome hg19 in paired-end mode and PCR duplicates removed using RmDup (Galaxy Version 2.0.1). Diffbind90 was used to identify differential H3K27ac marking of target regions using a p value cut-off of 0.05.

Hi-C matrices preprocessing
The raw paired-end reads were trimmed using TrimGalore! (https://github.com/FelixKrueger/TrimGalore) with default parameters. The resulting trimmed reads were aligned as single-end reads to the human genome hg19 Bowtie289 BAM files of technical replicates were merged using SAMtools (v1.15.1)91 and then processed using HiCExplorer (v3.7.2)92.
We used hicBuildMatrix (parameters --binSize 1000 --restrictionSequence GATC GAATC GATTC GAGTC GACTC --danglingSequence GATCAATCATTCAGTCACTC --minMappingQuality=1) to generate Hi-C matrices (h5 format) at a 1 kb resolution. The Hi-C matrices of biological replicates were merged with hicSumMatrices. The bin size was adjusted for different resolutions with hicMergeMatrixBins. CorrectMatrix was used to extract and filter out low-coverage regions. Quality control measures for Hi-C matrices were evaluated with hicQC, and the correlation between Hi-C matrices data was computed using hicCorrelate (parameters --method=pearson --zMin 0 --zMax 1 --plotNumbers). The distribution of long- and short-range contacts was calculated using hicPlotDistVsCounts.

A/B compartment annotation
HOMER tools93 were used for A/B compartment segmentation according to the documentation for all autosomes. A bin size of 50 kb was used with a window of 100 kb to calculate the first and second principal component (PC1 and PC2). The PC1 was used despite a few exceptions for chr 4, 9, and 16, where PC2 corresponded to the A/B classification. The signs for PC1 and PC2 were flipped according to H3K27ac ChIP-seq data. A replicate-based differential A/B analysis was performed using HOMER tools with FDR correction, a p value cut-off of 0.05, and an absolute log2(fold change) > 1 to identify domains with dynamic A/B status.

TAD identification
Hi-C matrices of 50 kb resolution were normalized using hicNormalize tool (--normalize smallest) of hicExplorer toolkit92. TADs were identified using hicFindTADs --thresholdComparisons 0.05). Average TAD-seperation scores were calculated using multiBigwigSummary (Galaxy Version 3.5.4+galaxy0) and Pearson correlation analysis was performed using plotCorrelation (Galaxy Version 3.5.4+galaxy0).

Hi-C viewpoint analysis
We analyzed genome-wide viewpoints to identify promoter-interacting regions using merged Hi-C matrices for each group. First, Hi-C matrices of 1 kb resolution were scaled to the smallest sequencing depth using hicNormalize. Viewpoints were generated for all gene promoters listed in the RefSeqSelect hg19 NCBI annotation. Promoter regions were defined as TSS ± 2.5 kb. Viewpoints with low coverage were filtered out using chicQualityControl (parameters --fixateRange 200000 -s 0.06). A set of 10,000 random genomic regions equivalent to the bin size was used to calculate and build a continuous negative binomial distribution model (background model). To exclude functional promoter interactions from the background model random regions passing were selected from inactive genome regions and filtered using chicQualityControl. The background model was calculated using chicViewpointBackgroundModel (parameters --fixateRange 200000 --averageContactBin 5). Background model adjustments were made to circumvent chromatin interaction overestimation owing to the sparse background signal in regions distant from the viewpoint. Bins exceeding 200 kb from the viewpoint were replaced with average background metrics derived from the last ten bins within the 200 kb adjacent intervals. The interaction frequency of 2 Mb region around each viewpoint was assessed with chicViewpoint (parameters --averageContactBin 5 --range 1000000 1000000). Interaction domains were determined by merging bins with relative interaction frequencies 1.5 fold above the background and interaction P values were recomputed. Regions not meeting the cut-off P value were rejected (chicSignificantInteractions with --pValue 0.0000000001 --range 1000000 1000000 --truncateZeroPvalues --fixateRange 200000 --xf 1.5 --peakInteractionsThreshold 10). The selected P value cut-off corresponds to a Bonferroni-corrected FDR P value of 0.005. Interaction domains adjacent to the center of each viewpoint (10 kb window) were excluded.

Visualization Hi-C plots
DeepTools94 and pyGenomeTracks94 integrated into the Galaxy platform95 were used to plot Hi-C heatmaps, viewpoints, RNA-seq, ChIP-seq, and ATAC-seq. For Hi-C, viewpoint analysis tracks were exported using chicExportData and heatmaps were generated from hic-ICE-corrected matrices of 5 kb bins.

Differential interaction analysis
We employed chicAggregateStatistic and chicDifferentialTest tools to identify potential differential promoter interactions between groups using merged Hi-C matrices (NF-LV-CM versus NF-LA-CM and NF-LV-CM versus F-LV-CM) with a significance level of 0.05 (chi-square test). A replicate post-hoc analysis was performed to ensure greater robustness. Therefore, we extracted the raw interaction data for the potential differential interactions and calculated the log2(fold change) and corresponding P value of the relative interactions (t-test). A P value of 0.05 was used as a cut-off criterion. The replicate-based analysis requires reproducibly covered regions. Therefore, we filtered out regions with a coverage of less than 1000 contacts per group. The gene expression was extracted for the obtained differentially interacting regions, and the fold change of gene expression was computed.

GWAS and eQTL analysis
We obtained genetic variants associated with human disease from the GWAS catalog49 (downloaded on May 30, 2023) and original studies50,51. We employed the Genomic Regulatory Elements and GWAS Overlap tool (GREGOR)96 to assess the enrichment of variants within specified regulatory elements while accounting for the structure of linkage disequilibrium (LD). Using GREGOR, proxy variants were identified in LD (r2 > 0.7) for each lead variant within a window of 1 Mb. LD scores were retrieved from the 1000 Genome project for the European population97. For statistical analysis of variants in target gene regions, the observed overlap of lead or proxy variants was compared to the expected overlap calculated using a random set of matched control variants. We assessed the enrichment based on the ratio of observed to expected overlaps. The significance of this enrichment was ascertained by assuming a sum of binomial distributions as implemented in GREGOR. A P value of less than 0.05 was considered significant.
The eQTL data used for this manuscript were downloaded from the GTEx Portal52 (V10) on 12/16/2024 (dbGaP accession number phs000424.vN.pN). The portal was used to plot gene-variant pairs.

Gene ontology (GO) and Genomic Regions Enrichment of Annotations Tool (GREAT)
Gene Ontology network analysis was conducted using the Cytoscape plugin ClueGO (v2.5.10)98. The analysis was based on the following criteria: Min GO Level = 4, Max GO Level = 8, Min number of Genes = 10, and Min Percentage = 8.0. Bonferroni step-down correction was applied to P values to control for multiple testing. The GO term connectivity threshold was set to 0.3 (kappa value), and only pathways with a P ≤ 0.01 were considered significant. The resulting terms were grouped by function, and the leading term each group was selected based on its significance. The findings were then illustrated using Cytoscape (v3.10.1).
Peak set gene annotation was done using GREAT (version 4.0.4)43,99 with the following parameters: Basal extension: 5000 bp upstream, 1000 bp downstream, 1,000,000 bp max extension, curated regulatory domains included.

MOTIF prediction
We performed de novo and known motif analysis using Homer93. We used TOBIAS TFBScan (v0.13.3)100,101 to call CTCF motifs (Jaspar: MA0139.1)102 in a set of peaks.

External data
RNA-seq data for NF-LA-CM was obtained from Ouwerkerk et al.103, and for NF-LV-CM, F-LV-CM from Gilsbach et al.11. ChIP-seq and DNA methylation data for F-LV-CM and NF-LV-CM were derived from Gilsbach et al.11. ATAC-seq datasets of NF-LV-CM and NF-LA-CM were derived from Hocker et al.19. Ventricular CTCF ChIP-seq (ENCFF053RDI) and DNA methylation data were obtained from the ENCODE portal104. All external data sets were reanalyzed starting from raw sequencing reads, apart from single-nucleus ATAC data, for which mapped data were used.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Haydar et al. provide Hi-C data for purified CMs from human non-failing LV and RA as well as from failing LV and combine this with their previously published epigenomic and gene expression data and use this to link chamber-specific chromatin interactions to gene expression programs and perform overlaps with variants from different diseases/traits. Overall, the Hi-C data is of good quality but limited to only cardiomyocytes with regards to cell type specificity. Though the finding of chamber-specific chromatin interactions is interesting, no evidence is provided to validate that any of the identified chamber-specific PIDs regulate chamber-specific gene expression. Furthermore, the authors validate CREs for two loci that are associated with QT duration, which given the authors’ identification of disease-specific PIDs, seems odd and not consistent with the studies on heart failure samples. Finally, the work is somewhat incremental to recent epigenomic and snATAC-seq studies on human hearts and/or cardiomyocytes (Zhang et al., PMID: 31427791, Anene-Nzeiu et al. PMID: 32866060, Avantis et al. PMID: 32111823, Foo studies PMID, Hocker et al. PMID: 33990324, Spurrel et al. PMID: 36130500 and Ameen et al. PMID: 36563664). As a result, the findings are somewhat descriptive and incremental to what has already been reported, thus limiting the impact and relevance of the findings. Overall, the manuscript in its current iteration has a number of issues that raise concerns about its suitability for Nature communication.\nMajor:\n1) Can the authors provide evidence that any of the identified chamber-specific PIDs drive chamber-specific gene expression, for example using transgenic reporter assays in mice?\n2) Why did the authors focus on QT-interval-associated CREs rather than HF-associated CREs for validation given that they identified PIDs that are specific to either failing or non-failing LV CMs? Were there no overlaps with HF variants for any of these PIDs? The lack of findings validating HF variants limits the relevance and significance of the findings.\n3) As changes in PIDs seem to account only for a subset of the gene expression changes, are there any changes in TADs/compartments between the different samples that could explain some of the gene expression differences?\n4) Fig. 3d-f: The increased interaction of the S-E with the NPPA promoter in F-LV-CMs is interesting but looks very subtle. Can the authors verify this interaction using a more quantitative method?\n5) Fig. 5: The authors should provide a table with target genes for the GWAS hits?\n6) The authors should compare findings to other recent human heart epigenomic and snATAC-seq studies by Zhang et al., PMID: 31427791, Anene-Nzeiu et al. PMID: 32866060, Avantis et al. PMID: 32111823, Foo studies PMID, Hocker et al. PMID: 33990324, Spurrel et al. PMID: 36130500 and Ameen et al. PMID: 36563664.\n7) Author should show that hiPSC cardiomyocyte model also display similar epigenetic landscape and chromatin interactions as human ventricular cardiomyocytes to show that hiPSC cardiomyocytes could be used as a model to test studied CREs. This is especially important as the authors have shown that there are significant differences in both epigenomic and chromatin landscape between differen types of cardiomyocytes.\n8) Knockouts of CREs and knockin of genetic variants should be performed to validate CRISPRi studies.\n9) Functional electrophysiologic studies should also be performed in hiPSC cardiomyocytes to confirm the impact of perturbing CREs.\n10) It is somewhat surprising that perturbing AR2 CRE within the SCN5a locus has no functional impact on SCN5a expression when the authors’ data and analyses suggest that it should, thus raising concerns about the utility of the authors’ findings.\nMinor\n1) Better figure labeling: e.g. for some figure panels one has to look at the figure caption to know what the different colors mean.\n2) Line 136-139: Define PID better (how is this different from a loop?). PIDs span 54 MB and overlap with 30k CREs spanning 24 MB. What is the remaining 30 MB if not CREs?\n3) Lines 161-165: This section is difficult to follow. It’s not clear how can you have diminished CTCF-mediated chromatin interactions without impacting higher-order chromatin organization? Are these CTCF sites not at TAD boundaries?\n4) Supplementary Fig 11: There’s a clear difference for NF-LA LMRs, but very marginal for NF-LV and F-LV. Can the authors do statistics for these comparisons? Furthermore, it could be interesting to plot ChIP-data, in particular H3K27ac, for the LMRs, as there maybe differences in enhancer activity that are not captured by CpG methylation.", "reviewer_2": "In this MS, the authors employ Hi-C to analyse chromatin interactions in healthy atrial and ventricular cardiomyocytes as well as failing ventricular CM. By also performing this analysis in ventricular tissue – which comprises multiple cell types, they illustrate that analysis specifically of the CM compartment is required to gain insight into how chromatin organisation can relate to other epigenetic marks and transcription in CM. This requirement to specifically analyse CM and not tissue builds on their elegant studies on cardiac epigenetics. In this current MS, the authors demonstrate chamber specific chromatin interactions that were related to associated transcription and transcription factor binding. Analysis of locations of SNP associated with cardiac disease determined these to be localised to chamber specific chromatin interacting regions. However, these did not appear to change with disease. Interestingly CRISPRi disruption of sites of interaction with the KCNJ1 locus that coincided with identified SNPs, decreasedKCNJ1 expression in iPS-CM suggesting functional importance.\nThis MS provides important new information regarding the regulation of gene expression across the chambers of the heart and how it may be affected by SNPs located at cis regulatory elements. Only through correlating these chromatin interactions specifically within CM and establishing the functional role of the CREs identified could these findings be reached.\nThere are some questions which require addressing:\n1- In Fig. 2d, only two groups are being compared, but the Kruskal-Wallis test was used. Since the Kruskal-Wallis test is generally used for comparing more than two groups, shouldn't the Wilcoxon rank-sum test be used instead?\n2- In Fig.3, the H3K27ac-marked super-enhancer is located upstream of both NPPA and NPPB, but the focus was primarily on NPPA, despite the RNA expression data showing that NPPB was also affected. Was this due to interactions of CRE only with the NPPA promoter? According to the Human cell atlas, both NPPA and NPPB are expressed in atrial cardiomyocytes, but NPPB is also expressed in some ventricular CM. Wouldn't it be valuable to discuss NPPB in more detail in the context of ventricular CM as well? It seems that the RNA peaks for NPPA and NPPB don not fully align with the gene locus. Perhaps the figure needs adjusting.\n3- In Fig. 5, analysis was restricted to promoter interacting CREs in non failing atrial and ventricular CM. This analysis identified enrichment for disease associated SNPs in these promoter interacting CRE that were related to disease in the related chamber. Would a stronger relationship with disease related SNPs be observed in PI-CRE in failing CMs.\n4- Similarly, it would be informative and add weight to the relevance of the interaction, if RNA levels, ATAC peaks, H3K27ac peaks were provided for KCNJ2 for failing as well as non-failing LV CM samples in Fig. 6a.\n5- In Fig 6b, the silencing of KCNJ2 promoter-interacting elements 3 and 4 significantly reduced KCNJ2 gene expression, yet Fig 6e shows no significant effect on the interaction frequency between these regions and the KCNJ2 promoter. Supplementary Fig 4 suggests there are only four differentially promoter-interacting domains (PIDs) for KCNJ2, possibly including elements 3 and 4. How can the silencing of interacting elements 3 and 4 reduce gene expression without affecting the promoter interaction frequency? What might explain this apparent disconnect between gene expression and promoter interaction?\n6- Low IK1 is a feature of iPS-CM contributing to their immature AP. Given that KCNJ1 is the contributing channel to this current, would its expression not also be basally very low in iPS-CM. The authors show relative change in expression of KCNJ1 following CRIPSRi. The relevance of these changes may however not be functionally important if expression is very low and may also overestimate the effect of CRISPR – i.e. CRISPR does not need to be very effective to reduce KCNJ1 expression. Could the levels of mRNA expression be shown and how do these compare to levels in adult vCM.", "reviewer_3": "In “Linking Chamber-Specific Spatial Chromatin Interactions to Disease Variants and Gene Programs in Human Cardiomyocytes” Haydar et al. conduct genome wide chromatin interaction mapping in ventricular vs atrial cardiomyocytes and ventricular cardiomyocytes from failing left ventricles. They then conducted multiple analyses related to cis regulatory element – promoter interactions and discovered chamber specific interaction that correlated with specific gene expression. Moreover, chamber specific interactions were more likely to be found in regions of disease associated genetic variants – e.g. AFIB associated variants linked with cis regulatory elements enriched in the atria vs ventricle. Additional CRISPRi experiments validated the importance of these promoter-cis regulatory element interaction on target gene expression. While this is not the first manuscript to associate genetic variants with cis regulatory elements, it is the first to show chamber specific cis regulatory elements and links to variants of chamber specific disease etiology. This work will impact workflows for investigating/validating genetic variants. These points are sufficiently impactful to indicate this will be a worthwhile addition to the literature and of appropriate scope for consideration by Nature Communications.\nThe biggest surprises in the data for this reviewer were that 1) largely the “…interactions were not affected in CMs of failing hearts and not linked to pathological gene expression on a genome wide scale.” And 2) CRISPRi of CREs failed to disrupt interactions despite impact on gene expression and chromatin accessibility (ATAC).\nThese findings could have profound implications for current models/understanding of gene expression regulation in the heart. Are the authors confident that the techniques used and data generated were sufficiently robust to draw these conclusions (positive control for 6e would help)? If so, please provide direct language in rebuttal explaining why and direct language in manuscript highlighting the implication of the findings.\nMinor issues indicated below:\n“Histone code” bar in multiple figures appears inaccurate in terms of RNA production.\nSentence from line 271-273 missing at least one word.\nWhat is the interpretation of interaction domain size difference between CM and Tissue 2C\nPlease articulate potential limitations of comparing current HiC to epigenetics data from another study.\n“A recent study shows that enhancer promoter interactions are becoming more instructive during muscle terminal differentiation.” Instructive to who/what?\nLabeling in some figures could be improved for interpretability – Suggest adding color code for groups to individual figure panels where appropriate.\nWhile the methods section is generally excellent, this reviewer has some confusion regarding the GWAS and variant vs proxy variant identification using a 1Mb window. For the proxy variants found at extreme distances shown to overlap CRE, was that CRE associated with a promoter nearer the lead variant? Comment may be outside of scope.\nAs indicated introduction contains a sentence indicating “interactions were not affected in CMs of failing hearts and not linked to pathological gene expression on a genome wide scale” yet discussion contains sentence indicating “Our data suggests that CTCF-mediated chromatin interactions are affected in terminal heart failure in CM” then goes on to discuss NPPA regulation. The “our data…” sentence should be qualified in some way to indicate lack of support for this observation in the genome-wide analysis."}, "author_response": "Thanks for reviewing the manuscript and suggesting experiments to increase its impact. As highlighted in detail in our responses to your points, we added substantial new experimental data and integrated published studies into our manuscript (as summarized on page 1). These experiments reinforce the biological and functional significance of our data on multiple layers. We believe that these updates markedly enhance the novelty and translational relevance of our study.\nMajor:\n1) Can the authors provide evidence that any of the identified chamber-specific PIDs drive chamber-specific gene expression, for example using transgenic reporter assays in mice?\nResponse:\nWe thank the reviewer for the important question. We addressed this question using complementary in vitro and in vivo approaches as detailed below.\nThe first evidence comes from transgenic reporter experiments recently published by Kosicki et al. NAR 20241. Out of 380 enhancers active in embryonic mouse hearts, more than 50% overlapped with adult CM PIDs. These enhancers were related to cardiac genes. Notably, 14% overlapped with chamber-specific PIDs identified in adult NF-LV-CMs. A major fraction showed concordant chamber-specific reporter activity and promoter-interactions, providing in vivo evidence that chamber-specific PIDs can confer chamber-specific activity.\nIn contrast, VISTA-positive enhancers that did not overlap with adult CM PIDs were enriched for genes involved in general or early developmental programs, suggesting that they may be active in embryonic but not adult heart (Fig. 3d,e).\nFurthermore, we perturbed chamber-specific PIDs in atrial or ventricular hiPSC-CM and could confirm the predicted regulatory activity. Silencing the ventricular-specific HEY2 PI-CRE in hiPSC-vCM reduced HEY2 expression and silencing atrial-specific PI-CREs associated with NPPA/NPPB and TBX5 in hiPSC-aCM resulted in significant reduction of their respective target genes (Fig. 4,5). These results demonstrate the functional contribution of these chamber-specific CREs to chamber-restricted gene expression.\nBy integrating eQTL data2 from human atrial appendages and left ventricle tissues, we identified genetic variants within chamber-specific PIDs impacting gene expression in a chamber-specific manner. Notable examples include CRE variants affecting NOS1AP and KCNK3 expression (Fig. 7 h-j).\nTogether, these additional lines of evidence support the conclusion that chamber-specific PIDs are an essential layer of gene regulation in human CM.\nWe updated the results sections accordingly (lines 215-228, 254-256, and 322-325) to clarify and strengthen this point. We also updated the methods section (lines 729-739, 747-748, and 911-913).\n2) Why did the authors focus on QT-interval-associated CREs rather than HF-associated CREs for validation given that they identified PIDs that are specific to either failing or non-failing LV CMs? Were there no overlaps with HF variants for any of these PIDs? The lack of findings validating HF variants limits the relevance and significance of the findings.\nResponse:\nWe thank the reviewer for raising this point. Considering that disease-associated genetic variants are enriched in HF-associated CREs would imply that the genetic burden is modified by the onset of heart disease. We performed a GWAS analysis for HF-associated CREs and did not find a clear enrichment of genetic variants for any tested genetic trait. This suggests that HF-associated CREs are not modulating genetic disease risk factors in human LV-CM.\nIn contrast, a strong enrichment for QT-associated variants was observed specifically in NF-LV-CM, driving our choice to focus on these genetic risk factors in this manuscript. The results of our GWAS analysis are summarized in Reviewer Fig. 1, which is attached below.\nReviewer Fig. 1: Heatmap displaying the significance of the enrichment calculated for genome-wide association study (GWAS) variants compared to random variants for NF-LV-CM PI-CREs and differential PI-CREs in F-LV-CM as compared to NF-LV-CM. The results for various organ disease traits from the NHGRI-EBI GWAS catalog are shown.\nTo further highlight HF-associated CREs, we added a new main figure to the manuscript (Fig. 6) showing promoter rewiring, H3K27ac-dynamics, and associated gene expression changes in F-LV-CM. We could not identify a suitable in vitro model to test the impact of rewiring or marking of CRE in heart disease. However, we silenced two candidate CREs using CRISPRi in hiPSC-CM that were picked based on either gained promoter interaction strength (NPPA) or lost H3K27ac-marking (HEY2) in F-LV-CM as compared to NF-LV-CM. Silencing of either CRE reduced expression of their respective target gene, indicating the functionality of these HF-associated CREs (Fig. 4,5). We updated the results sections accordingly (lines 254-256 and 273-282).\n3) As changes in PIDs seem to account only for a subset of the gene expression changes, are there any changes in TADs/compartments between the different samples that could explain some of the gene expression differences?\nResponse:\nTo address this question, we performed a comparative analysis of TADs and A/B chromatin compartments across all CM datasets. TADs are highly conserved across all CM groups. TAD insulation scores were strongly correlated, and TAD boundary strength metrics showed minimal variation between NF-LV-CM, NF-LA-CM, and F-LV-CM (Supplementary Fig. 11). Similarly, A/B compartmentalization was largely stable. Principal component analysis of the Hi-C contact matrices revealed only limited A/B switching: 18 autosomal regions (spanning ~3 Mb, ~0.1% of the autosomal genome) differed between NF-LV-CM and F-LV-CM, and 7 regions (~3.2 MB, ~0.1% of the autosomal genome) differed between NF-LA-CM and NF-LV-CM (Supplementary Fig. 11). Importantly, these compartment shifts were not linked to changes in histone modifications or gene expression.\nThese findings indicate that gene expression differences according to chamber identity and disease are not driven by global chromatin architecture remodeling (TADs, A/B compartments).\nWe have added this analysis and the supporting figures to the revised manuscript results section (lines 146-157), methods section (lines 841-856) and added Supplementary Fig. 11.\n4) Fig. 3d-f: The increased interaction of the S-E with the NPPA promoter in F-LV-CMs is interesting but looks very subtle. Can the authors verify this interaction using a more quantitative method?\nResponse:\nWe thank the reviewer for this point and addressed it in two ways in the revised manuscript: we first perturbed the central super-enhancer domain using CRISPRi in hiPSC-aCM to validate the functional relevance of this interaction (Fig. 5f,g).\nSecond, we fine-mapped the altered interaction pattern of the super-enhancer with the NPPA promoter using a tiled super-enhancer viewpoint analysis (1kb resolution), as shown in the new Fig. 5d. This analysis quantifies the differential interaction of different super-enhancer domains with the NPPA promoter.\nWe agree that the differences are visually minor at first glance due to the high background level, typically for regions proximal to the viewpoint. In the revised version, we therefore highlight the background levels in the bar plots of our replicate-based HiC analysis, too. Most Hi-C studies performed statistical analysis using Hi-C data merged from multiple biological replicates, which does not account for biological and technical variability. The individual values resulting from the biological replicates displayed in the bar and scatter plots are reproducible, highlighting the reliability and quantitative nature of Hi-C. To our knowledge, this is the first replicate-based analysis of promoter-enhancer interactions, which was made possible by the new version of HiCExplorer with features developed as part of this project. This aligns with recent work comparing Hi-C-based methods to alternative approaches to study chromatin interactions. The quantitative nature of Hi-C data was confirmed by two recent preprints demonstrating highly correlating interaction data between Hi-C and imaging data from DNA-FISH or fluorophores tagged to interacting loci3,4. However, DNA-FISH showed a lower signal-to-background ratio than HiC3. Therefore, imaging-based methods are not a method of choice for detecting differences between cell states or subtypes, like LA-CM and NF-CM, as studied in this manuscript. Among the different “C-methods”, high-resolution Hi-C is the gold standard for detecting chromatin interactions if the sequencing depth is high and a frequent cutter is used5. We used two frequently cutting restriction enzymes with the recognition motifs GATC and GANTC and applied deep sequencing per replicate. We would agree that it is, in general, hard to quantify the interaction strengths of multiple regions interacting with the same promoter, e.g., a distal and a proximal enhancer, due to the bimodal distribution of the signal. Nevertheless, we think Hi-C does allow reliable quantitative detection of interaction strength across samples.\nWe have updated the results section (lines 247-256), and added Fig. 5.d,f,g.\n5) Fig. 5: The authors should provide a table with target genes for the GWAS hits?\nResponse:\nIn the revised manuscript, we provide comprehensive lists of PI-CREs (Supplementary Table 7-9) along with PIDs detected for each condition. This allows identification of candidate CREs and target genes by simple intersection with variant coordinates. We opted not to include processed PID-variant intersections as providing such tables for the different conditions and all GWAS data (including sub-threshold variants) studied here would require many large supplementary files, which would make integrating novel variants detected in new GWAS studies hard. Instead, this format provides a more flexible and user-friendly resource for ongoing and future variant annotation efforts.\n6) The authors should compare findings to other recent human heart epigenomic and snATAC-seq studies by Zhang et al., PMID: 31427791, Anene-Nzeiu et al. PMID: 32866060, Avantis et al. PMID: 32111823, Foo studies PMID, Hocker et al. PMID: 33990324, Spurrel et al. PMID: 36130500 and Ameen et al. PMID: 36563664.\nResponse:\nWe thank the reviewer for this list of relevant manuscripts related to cardiac epigenetic mechanisms.\nIn the revised version of the manuscript, we integrate the original ATAC data for NF-LV-CM and NF_LA-CM from Hocker et al. (PMID: 33990324). We also noted that, unlike our study, this paper did not report chamber-specific variant assignment to CRE in atrial and ventricular CM, a point we also discussed with co-corresponding author Sebastian Preißl.\nThe second paper listed from the Ren and Chi labs (with Sebastian Preißl) studied chromatin organization during ES cell to CM differentiation, which is not directly linked to our manuscript centered on adult CM chromatin architecture. As acknowledged in response #7, we plan to do an in-depth analysis of ES-/iPS-derived CM data in comparison to in vivo CM data in a separate study, since this comparison is beyond the scope of this manuscript.\nIn the course of this revision, we cross-validated the enhancer deletions performed by Anene-Nzelu et al. (PMID: 32866060) using CRISPRi (Supplementary Fig. 14). In an earlier version of this manuscript, we had included cardiac tissue Hi-C data kindly shared by Roger Foo (DOI: 10.21203/rs.3.rs-1897221/v1). Following concerns about potential methodology-induced biases for comparison and to increase sequencing depth we replaced it with original data from tissue matching the sorted CM samples. Nevertheless, both tissue datasets look identical and show the same differences compared to CM-specific data, highlighting the benefit of cell type-specific analysis and that HiC is reproducible even across different labs and protocols.\nThe snATAC study of Ameen et al. PMID: 36563664 focuses on the genetics of congenital and developmental processes. This manuscript shows the function of variant enhancers in endothelial cells for the development of CHD and characterizes the regulatory landscape of iPS-CM compared to fetal atrial and ventricular CM. This comparison shows a high correlation of chromatin accessibility between iPS-derived and fetal CM. We discuss this paper in the revised version as part of the introduction and limitation section.\nAvantis et al. PMID: 32111823 reports a variant (rs535411) overlapping with an accessible region in iPS-CM. They linked this variant to ACTN2 using ES-CM Hi-C data. We analyzed this locus, and the variant region is accessible in NF-LV-CM, too. However, the interaction with the ACTN2 promoter is not very strong and does not reach our detection cut-offs. Remarkably, a neighboring variant rs1544970 is associated with an ATAC peak in NF-LV-CM and shows a chamber-specific interaction with the ACTN2 promoter in NF-LV-CM, orchestrating the increased ACTN2 expression in NF-LV-CM as compared to NF-LA-CM.\nWe have extended our discussion to highlight variants impacting cardiac gene expression confirmed by enhancer deletion.\nWe cite Spurrel et al. PMID: 36130500 and Tan et al. Circulation (Foo lab) together with (Dickel et al. Nat. commun.) for the generation of cardiac H3K27ac data of diseased hearts in the introduction. We did not integrate the original data due to the availability of CM-specific data spanning fetal and postnatal maturation and heart failure6.\nWe have done our best to integrate suggested cardiac epigenetic studies related to this work, reflecting on the new comparisons and emphasizing where our findings extend beyond these prior studies.\n7) Author should show that hiPSC cardiomyocyte model also display similar epigenetic landscape and chromatin interactions as human ventricular cardiomyocytes to show that hiPSC cardiomyocytes could be used as a model to test studied CREs. This is especially important as the authors have shown that there are significant differences in both epigenomic and chromatin landscape between differen types of cardiomyocytes.\nResponse:\nWe thank the reviewer for this important point. We agree that hiPSC-CM are not fully mature and do not fully recapitulate the epigenome of adult CM. Given that an in-depth analysis of chromatin and epigenetic conservation across the entire genome is outside of the scope of this manuscript, we have not included this data but plan to address this in a future study. We acknowledge that differences in the epigenetic and chromatin interactions exist, hence a functional analysis in hiPSC-CM or specific subtypes is relevant when the putative CRE-target interaction and the epigenetic states are conserved with adult CM.\nResults shown in Reviewer Figure 2 highlight that 76% of PIDs identified in NF-LV-CM were also detected in hiPSC-vCM (a), indicating substantial homology in PID formation. In addition, CpG methylation patterns of PI-CRE detected in NF-LV-CM are highly similar in hiPSC-vCM (b), supporting the conservation of regulatory elements. We further compared the relative PID interaction strength and the number of PIDS for CM marker genes listed in Supplementary Table 1. This comparative analysis did not reveal significant differences between NF-LV-CM and hiPSC-vCM (c,d).\nThese findings suggest a high degree of conservation at a large set of functionally relevant loci, supporting the use of hiPSC-CM for CRISPRi-based functional validation. However, we have included a statement in the limitation section acknowledging this point (lines 457-465).\nReviewer figure 1: Comparative analysis of LV-NF-CM and hiPSC-vCM\na) Fraction of NF-LV-CM PIDs detected in hiPSC-vCM\nb) CpG methylation heatmap of PI-CRE detected in NF-LV-CM and flanking regions. Plotted are CpG methylation data for NF-LV-CM and hiPSC-vCM.\nc,d) Cumulative relative interactions and number of PIDs per CM marker gene (112 genes, PanglaoDB7) detected in NF-LV-CM and hiPSC-vCM. Two-tailed Mann-Whitney test.\n8) Knockouts of CREs and knock-ins of genetic variants should be performed to validate CRISPRi studies.\nResponse:\nWe thank the reviewer for suggesting these control experiments. To validate our CRISPRi silencing methodology, we performed the following experiments and analysis:\n1) Cross-validation of published enhancer deletion studies using CRISPRi: We silenced enhancers of MYL2, MYH7, SCN5A, and KCNH2 using CRISPRi. CRISPRi decreased gene expression of the respective target genes, mimicking the previously reported effects observed upon enhancer excision in vitro and in vivo8-11 supporting the robustness of CRISPRi for interrogation of CRE function (Supplementary Fig. 14, Supplementary Fig. 15).\n2) Targeted KCNJ2-CRE ablation to validate CRISPRi: In this study, we identified a functional KCNJ2 enhancer (region 4) using CRISPRi. We deleted this region using CRISPR/Cas9-mediated genome editing in hiPSCs. Two independent deletions were generated followed by CM differentiation and both led to a significant downregulation of KCNJ2 in differentiated hiPSC-vCM, consistent with our CRISPRi results. (Supplementary Fig. 21).\nTo study the impact of genetic CRE variants on a global scale, we integrate PID data with cardiac eQTL data2. This identified 7608 genetic PI-CRE variants affecting gene expression of the interacting gene. As shown in the new Fig. 7h-j, genotype-dependent expression differences were even observed in a chamber-specific manner. For example, rs12028141, a variant overlapping a ventricular PI-CRE, was significantly associated with NOS1AP expression in the left ventricle (P value = 4.14e-8) but not in the atrial appendage. Likewise, rs12468863 was linked to KCNK3 expression specifically in the atrial appendage (P value = 8.4e-7) but showed no significant association with KCNK3 expression in the ventricles.\nWe updated the results sections accordingly (lines 187-193, 344-345, and 322-325), and the methods section (lines 693-713, 729-739, 747-748, and 911-913).\n9) Functional electrophysiologic studies should also be performed in hiPSC cardiomyocytes to confirm the impact of perturbing CREs.\nResponse:\nWe thank the reviewer for this critical and challenging suggestion which led to novel insights.\nTo directly assess the functional impact of CRE perturbation at the electrophysiological level, we performed automated patch-clamp recordings in hiPSC-vCM following CRISPRi-mediated silencing of the KCNJ2 promoter and two PI-CREs (region 3,4). KCNJ2 encodes the inward-rectifier potassium channel Kir2.1. Silencing of the KCNJ2 promoter led to 90% reduction in IK currents, validating the methodology. Silencing of the distal PI-CREs region 1 3 and 4 resulted in 39% and 67% reduction in IK1 current, respectively. These effects closely mirrored the impact of CRISPRi on KCNJ2 mRNA levels. We further checked whether silencing of region 3 and 4 affects the repolarization time using multi-electrode arrays. We observed a prolonged repolarization time compatible with the inhibition of the Kir2.1 currents, highlighting the physiological function of these regions.\nThese results show for the first time that these distal CREs contribute to functional regulation of ion channel activity (Fig. 8, Supplementary Fig. 20).\nThese results and the additional CRISPRi experiments performed for the revised manuscript highlight that combining cell-type-specific epigenome and chromatin interaction data with functional perturbation can provide novel insight into the physiological relevance of disease-associated genetic variants.\nWe updated the results sections accordingly (lines 349-356), and the methods section (756-773).\n10) It is somewhat surprising that perturbing AR2 CRE within the SCN5a locus has no functional impact on SCN5a expression when the authors’ data and analyses suggest that it should, thus raising concerns about the utility of the authors’ findings.\nResponse:\nWe appreciate the reviewer's careful attention to this point. Although AR2 is predicted to interact with the SCN5A promoter, its silencing with CRISPRi had no significant impact on SCN5A expression. However, silencing of the neighboring interacting AR1 element did significantly reduce SNC5A gene expression. The Christoffels lab11 reported comparable results for corresponding regions in mice, highlighting a functional evolutionary conservation of these enhancer domains.\nWe interpret the lack of efficient silencing via AR2 not as a contradiction to our approach but rather as an illustration of the context-dependent and non-binary nature of CRE function. AR2 activity may be redundant with nearby regulatory elements, developmental stage-specific, or active in specific conditions. It has been reported that not all CREs are detected to be functionally active under baseline conditions for several biological systems. Chromatin interaction data and epigenetic data are essential to prioritize active CRE candidates. During the revision, we silenced 9 additional CREs, and only one did not significantly reduce target gene expression. This high fraction of positive read-outs with this approach exceeds, in our experience, the outcome of studies targeting CREs without prior knowledge of epigenetic and chromatin interaction data. In the case of these loci, CRISPRi had no detectable impact on chromatin organization (Supplementary Fig. 14), Since it can be applied to matured hiPSC-CM, our CRISPRi strategy also avoids interfering with the cell differentiation process.\nMinor\n1) Better figure labeling: e.g. for some figure panels one has to look at the figure caption to know what the different colors mean.\nResponse:\nWe thank the reviewer for the critical note. We have updated all figures to include the color legends.\n2) Line 136-139: Define PID better (how is this different from a loop?). PIDs span 54 MB and overlap with 30k CREs spanning 24 MB. What is the remaining 30 MB if not CREs?\nResponse:\nOur study defines promoter-interacting domains (PIDs) as continuous genomic intervals exhibiting statistically significant interaction with a given gene promoter based on virtual viewpoint analysis. These domains are incrementally increased during the analysis, starting from an individual bin. This analysis is performed independently from the annotation of regulatory elements. Therefore, PIDs do not necessarily overlap with CRE elements. In reality, most overlap with individual or multiple CREs, interspaced, e.g., by non-regulatory chromatin. This allows the detection of chromatin interactions independent of epigenetic marking. We have integrated additional plots into Supplementary Fig. 13 visualizing the chromatin state of PIDs, and CREs in more detail to address your question.\nThis approach differs from e.g. the ABC model, which integrates different data modalities and reports loops based on H3K27ac marking of individual ATAC peaks.\nIn contrast to PIDs, loops are discrete point-to-point contacts and reflect mostly interacting TAD boundaries.\n3) Lines 161-165: This section is difficult to follow. It’s not clear how can you have diminished CTCF-mediated chromatin interactions without impacting higher-order chromatin organization? Are these CTCF sites not at TAD boundaries?\nResponse:\nWe agree that we did not explain the related supplementary figure in detail. In the revised version, we extended this section in the main manuscript to improve clarity. Indeed, only 8% of differential PIDs (233) overlap TAD boundaries. This may explain the lack of dynamic TADs. So far, we cannot explain the relevance of the CTCF-positive PIDs in failing CM, given that they are unrelated to gene expression changes. Further studies are needed to clarify the underlying mechanisms. We therefore report this observation only as part of Supplementary Fig. 18.\n4) Supplementary Fig 11: There’s a clear difference for NF-LA LMRs, but very marginal for NF-LV and F-LV. Can the authors do statistics for these comparisons? Furthermore, it could be interesting to plot ChIP-data, in particular H3K27ac, for the LMRs, as there may be differences in enhancer activity that are not captured by CpG methylation.\nResponse:\nWe changed the analysis to a replicate-based method (Metilene12). These results confirm that CpG methylation of CREs is minimally affected in failing CM as we have already reported for LMRs and additional genomic elements using an alternative annotation strategy6. In contrast, CpG methylation of CREs in LA and LV CM shows substantial differences. We added this analysis to the revised manuscript (Supplementary Fig. 12).\nAs suggested, we now also include an analysis of differential H3K27ac marking of CREs in failing CM and link these changes to differential gene expression using our chromatin interaction data (Fig. 6c-d). We confirm the functional association for a dynamically 27ac-marked HEY2 CRE using CRISPRi (Fig. 6e and Fig. 4c).\nWe updated the results sections accordingly (lines 273-282), and the methods section (lines 799-801, and 819-820)."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have made significant efforts to address the reviewers' comments.\nI am satisfied with the answers to my comments and the modifications to the MS.\nI have no remaining issues.", "reviewer_3": "Authors have addressed my concerns. Will be a solid addition to the literature.", "reviewer_4": "The authors have addressed my concerns.", "reviewer_5": "In this study, the authors investigate chamber-specific cis-regulatory elements (PID-CREs) that interact with their target genes using good quality Hi-C data. They identify PID-CREs that are specific to atrial and ventricular tissues and demonstrate their role in regulating chamber-specific marker genes by CRISPRi. Furthermore, the study highlights an enrichment of SNPs within these regulatory elements that are associated with arrhythmia-related GWAS signals, including those linked to long QT syndrome, thereby providing a valuable framework for investigating and functionally validating disease-associated genetic variants.\nThe authors have carefully and thoroughly addressed all of Reviewer 1’s comments. The revisions prompted by these comments have significantly strengthened the manuscript. Notably, the authors implemented several key improvements that enhance the robustness and functional relevance of their findings:\nAs recommended by Reviewer 1, the authors performed CRISPR interference (CRISPRi) targeting chamber-specific PID-CREs in atrial and ventricular hiPSC-derived cardiomyocytes (hiPSC-CMs), which led to changes in the expression of target genes.\nThey validated one of the CRISPRi results by performing complementary CRE deletion experiments, which yielded consistent outcomes.\nIn addition, the authors incorporated new functional data to support the impact of CRE perturbation in iPSC-CMs. Specifically, they conducted electrophysiological experiments, presented in Figure 8, which functionally validated two CREs at the electrophysiological level.\nIn their response, the authors also referenced recently published studies that corroborate their findings, particularly regarding the presence and relevance of PID-CREs.\nMinor comment:\nIn their CRISPRi experiments, the authors used atrial and ventricular cardiac differentiation hiPSC. It would be helpful to report the enrichment of atrial and ventricular cardiomyocytes obtained from each respective differentiation, to provide better context for the interpretation of the functional perturbation results.\nOverall, I find the authors’ responses to be adequate, and the revised manuscript acceptable for publication in its current form. I therefore recommend acceptance."}, "author_response": "Reviewer #2 (Remarks to the Author):\nIn response to the reviewers’ comments, the authors have responded with a substantial revision of their MS including new data.\nI am generally satisfied with the responses to my questions.\nAnswer: Thanks for the positive feedback on the revision of our manuscript and the confirmation that our additional data addressed your questions.\nOne remaining issue – I, as well as other reviewers comment on the choice of KCNJ2 (and its enhancers) as a target that is followed up upon with functional assays. This is followed up on as notably, the authors do not show any HF related interactions – perhaps owing to insufficient sample number.\nAnswer: Indeed, we did not observe significant enhancer rewiring at the KCNJ2 locus in HF samples (Supplementary Fig. 20). For this analysis, we included n = 8-9 samples, which provides robust statistical power. While we emphasize the importance of annotated enhancers due to their regulatory significance and enrichment in LQT-associated variants, we cannot rule out the possibility that rewiring of this locus is disease-relevant in a subset of HF cases. Testing this hypothesis would require substantially larger sample cohorts, as we now state in the revised limitations section.\nNotably, experiments were performed to knockdown expression of a transcription factors and NPPA but consequences for hypertrophic remodelling was not performed.\nAnswer: Thanks for highlighting the results added during the revision. We agree that perturbing enhancer elements holds the potential to modulate hypertrophic remodeling and HF phenotypes. We tested different hiPSC-CM hypertrophy models but have not yet identified an in vitro system that allows for HF-related phenotypic assessment following enhancer perturbation. Given these limitations of current human cellular models of HF, this aspect lies beyond the scope of this study.\nGiven this less related analysis of KCNJ2 to HF and link to LQT rather than HF related genes, I would modify the Intro and discussion.\nSudden cardiac death is commonly associated with HF as are other arrhythmic disease, which cardiomyocytes may be predisposed to under conditions of altered KCNJ2. This link to arrhythmia should be made rather than solely focus on HF.\nBut – this arrhythmia may also be a consequence of hypertrophic remodeling. It may also be argued that a longer APD could signal induction of a hypertrophic response. This could be discussed.\nAnswer: We agree, HF and LQT are linked risk factors of arrhythmia and represent mutual risk factors resulting in an overlapping syndrome including a predisposition for sudden cardiac death. We discuss this interplay in the revised discussion. However, the genetic risk factor signals of HF- and Arrhythmia-related traits are distinct. Consistent with this, we didn’t observe chromatin rewiring at QT-associated loci in HF, as analyzed in response to points raised by the reviewers in the first revision round and visualized for the KCNJ2 locus in Supplementary Fig.20. Testing the impact of LQT on chromatin organization is of high interest, but is currently beyond the scope of this manuscript due to the inaccessibility of cardiac tissue from patients with an LQT syndrome.\nIn the initial version of the manuscript, HF was only part of the supplementary material (despite one plot related to NPPA). We extended the HF-related analysis and incorporated it into the main manuscript following requests from all three reviewers.\nIn line with your advice, we modified the discussion and included the limitations regarding the HF-associated rewiring in the corresponding section. Furthermore, in the current version of the abstract, we have limited the mention of HF-related analysis to one sentence.\nReviewer #3 (Remarks to the Author):\nAuthors have addressed my concerns. Will be a solid addition to the literature.\nAnswer: Thank you for the positive feedback, reviewing the manuscript and recommending publication.\nReviewer #4 (Remarks to the Author):\nThis study employs advanced multi-omics techniques (Hi-C, Methyl-seq, ChIP-Seq, ATAC-seq) to comprehensively investigate the epigenomics and 3D chromatin architecture of healthy human atrial and ventricular cardiomyocytes (CMs) as well as failing ventricular CMs. The authors functionally validated promoter-interacting CREs of the MYL2, MYH7, KCNH2, and SCN5A, and prototypic KCNJ2 genes in hiPSC-CMs using CRISPR interference (CRISPRi) approaches. These multi-omics efforts reflect tremendous work, and the additional data provided to address previous reviewer comments have strengthened the manuscript. However, several key issues still need to be addressed before publication.\nAnswer: Thank you for the positive feedback and for reviewing the manuscript.\nMajor\n1. Unclear central goal: The current manuscript doesn’t clearly articulate the central goal, hypothesis, or main research question. This lack of clarity makes it difficult to follow the purpose of the individual sub-sections in the Results. Please revise the abstract, Introduction and Results sections to clearly define the study’s overarching objective.\nAnswer: Thank you for this suggestion. We revised the indicated chapters as suggested to clearly state the overarching study aim and the objectives, as well as the conclusions.\n2. Value added by chamber-specific Hi-C: From the title, it appears the authors intend to demonstrate that chamber-specific chromatin architecture revealed by Hi-C provides unique insights for functional interpreting disease associated Cis-regulatory elements. If this is correct, it would be helpful to explicitly summarize key discoveries that were uniquely revealed by chromatin Hi-C sequencing, but not by other epigenetic or transcriptomic profiling.\nAnswer: Previous studies using epigenetic analysis of heart tissue or cardiomyocytes did not show a difference in GWAS signals in atrial versus ventricular specimens, even in a snATAC-seq study of human atrial and ventricular tissue1. Our integration of chromatin interaction and epigenetic data fills this gap by demonstrating that chamber-specific interactions not only explain differential gene expression but also uncover a distinct enrichment of GWAS signals in promoter-interacting regulatory elements of atrial and ventricular CM. This distinct enrichment of GWAS variants is causally linked to the disease mechanism.\nImportantly, Hi-C data were essential to link disease-associated CREs to their distal target genes, since more than 77% of CRE-Promoter interactions span single or multiple genes, and this fraction rises to 86% for CREs harboring sub-threshold QT-associated variants. We added these findings to the revised manuscript (Supplementary Fig. 13e), and expanded the discussion to emphasize the unique added value of Hi-C.\n3. Hi-C resolution concern: On page 5 line 151, the manuscript states that “principal component analysis of the Hi-C contact matrices revealed minimal differences in chromatin compartmentalization among groups”, and the author conclude that “both TAD structure and A/B compartmentalization are highly conserved across CM subtypes despite associated transcriptional and epigenetic changes” (line 157). However, considering that a 1 kb bin size was used for Hi-C matrices preprocessing, but larger bin sizes (50kb for A/B compartment and 100kb for TAD) were used for downstream analyses, this raises the possibility that the chosen resolutions may not be sufficient to detect chromatin architecture changes in diseased hearts. Please justify the chosen resolutions and discuss their limitations.\nAnswer: Thanks for the comment. Topologically-associating domains (TADs) are large genomic domains, typically between 500 kb–1 Mb3, and therefore do not require very high resolution for robust identification. For the initial analysis, we used 100kb bin matrices. Following your comment, we repeated this analysis using smaller bin sizes (10-50 kb) and did not identify alterations in TAD structures, supporting our initial analysis. Notably, we used HiCExplorer4 for the identification of TADs. HiCExplorer utilizes windows of varying bin sizes to ensure reliable identification of TADs. We updated the related figures and plot data obtained using 50kb bin sizes.\nWe further confirmed the results visually using a range of bin sizes, since differential TAD analysis is challenging if the expected differences are minor compared to those between cell types. Using this approach, we confirmed the absence of alterations in TAD structures between groups and samples.\nA/B compartments span individual or multiple TADs. For the identification of A/B compartments, we used HOMER5 using the default resolution of 50kb. This resolution is widely established for identifying these multi-megabase chromatin structures3. Lowering the resolution exponentially increases the computing time without providing additional insights due to the large genomic distances of A/B compartments. The selected bin sizes are thus suitable for obtaining reliable information about these higher-order chromatin structures.\nFurthermore, based on our experience, a TAD and A/B compartment analysis using the tools employed in this study with Hi-C data at high resolution (1-5 kb bins) yields unintentional identification of rewiring events/switches to the level of individual loops, including CRE-promoter interactions, which supports the chosen larger bin sizes. As noted in the revised limitations, new Hi-C analysis methods may reveal additional chromatin organization layers and features in future work. The deep data presented in this study would provide a valuable resource for such studies too. We updated the Supplementary Fig. 11 and the corresponding sections in the main manuscript accordingly.\n4. Suitability of hiPSC-CMs for functional validation: hiPSC-CMs were used for functional validation experiments. Since the authors acknowledge the maturation limitations of hiPSC-CMs, it would strengthen the study to compare the 3D chromatin architecture of primary CMs and hiPSC-CMs, particular at the loci studied, to assess whether hiPSC-CMs are a suitable model for those chromatin architecture functional assays.\nAnswer: We thank the reviewer for this important point. We agree that hiPSC-CM are not fully mature and do not fully recapitulate the epigenome of adult CM. Given that an in-depth analysis of chromatin and epigenetic conservation across the entire genomic landscape is outside of the scope of this manuscript, we have not included these data, but plan to address this in a future study.\nIn Reviewer Figure 1,2, we represent the promoter interactions of all the perturbed loci shown in Fig. 4c,d (HEY2, TBX5), Fig. 5f,g (NPPA, NPPB) and Fig.8b (KCNJ2) in hiPSC-vCM or hiPSC-aCM. These confirm that the interactions of the studied regions are conserved between primary and hiPSC-derived CM.\nReviewer Figure 3 highlights that 76% of PIDs identified in NF-LV-CM were detected in hiPSC-vCM (a) on a genome-wide scale, indicating substantial homology in PID formation. In addition, CpG methylation patterns of PI-CRE detected in NF-LV-CM are highly similar in hiPSC-vCM (b), supporting the conservation of regulatory elements. We further compared the relative PID interaction strength and the number of PIDs for CM marker genes listed in Supplementary Table 1. This comparative analysis did not reveal significant differences between NF-LV-CM and hiPSC-vCM (c,d). These findings suggest a high degree of conservation at a large set of functionally relevant loci, supporting the use of hiPSC-CM for CRISPRi-based functional validation studies.\nTherefore, we have included a statement acknowledging the immaturity of hiPSC-CMs as a limitation.\n5. This might be out of the central scope of this manuscript. But to support the conclusion that “CRISPRi precisely silences individual regulatory elements”, an additional CRISPRi group targeting region 3 should be included in Fig. 8d.\nAnswer: We targeted region 3 using CRISPRi, and the results confirmed the specificity of the perturbation. These findings are in line with the individual silencing of region 4 and the combinatorial silencing of regions 3+4 depicted in Fig. 8 and with results previously reported by our group8. We have updated Fig. 8d as suggested.\n6. The action potential duration (APD) varies significantly due to difference in ion channel expressions among individual hiPSC-CMs. The sample size (N = 6) in Fig. 8f is too small to draw a strong conclusion. It would be helpful to include CRISPRi target region 1 (promoter), which completely silenced gene expression (Fig.8e), as a positive control group in Fig.8f. In addition, patch clamp recordings using SyncroPatch 384 might be more sensitive than MEA for detecting APD changes and should be considered.\nAnswer: As suggested, we increased the sample size and performed MEA recordings following CRISPRi-mediated KCNJ2 promoter silencing. These experiments revealed a significantly prolonged AP duration, consistent with the gene silencing effect and the reduced Kir2.1 currents measured using patch clamp (Fig. 8). Notably, the AP extension observed after KCNJ2 promoter silencing exceeds the AP extension detected after silencing of individual enhancers (region 3, 4), as expected given that CRISPRi of the KCNJ2 promoter resulted in effective (>99%) silencing of KCNJ2 expression.\nThe MEA technology measures currents from a syncytium of cardiomyocytes in a 2D monolayer, rather than from individual cells. This explains the lower variability of data points obtained from MEA experiments compared to patch clamp measurements, which detect Kir2.1 currents in individual cells.\nFor the revised manuscript we generated new MEA data, which were analyzed using the \"Cardiac Analysis Tool (Axion Biosystems)\" that allows for the review and correction of the Q- and T-wave annotations provided by the \"AxIS Navigator (Axion)\" software. This analysis confirms the previous observations.\nIn contrast to MEA experiments, which take advantage of spontaneous depolarization events, patch clamp recordings of action potentials require the injection of current. Automated patch clamp can measure ion channel currents accurately but has proven inefficient at capturing action potentials9 because of the high access resistance that results in a voltage drop10. This implies that current-clamp measurements should be interpreted with caution, as both the amplifier circuitry and low seal resistances can necessitate high amounts of injected current and produce artificial readouts of resting membrane potentials, and therefore possibly misreport AP morphology. We therefore believe that additional patch clamp measurements for the detection of AP duration are not of importance for the current manuscript. We want to note that AP data cannot be extracted from the existing patch data generated in this study.\nIn the revised manuscript, we added new MEA data generated during the second revision, including data for CRISPRi-mediated promoter silencing (region 1), as suggested.\nMinor\n1. Please provide justification for why only male cardiac biopsies were used in this study.\nAnswer: We agree that data from female biopsies would be essential to analyze and highlight sex-dependent mechanisms of chromatin organization, as documented for non-CM cells. However, as stated in the limitations of our study, we were unable to collect a matching cohort of female biopsies. We especially lack suitable age-matched non-failing and non-diseased tissue samples that would allow such analysis.\n2. Lin767 states: “hiPSC-vCM were seeded at a density of 30,000 cells in 5 μl media onto Geltrex-coated recording electrodes of a 48-well CytoView MEA plate (Axion Biosystems) using the spot seeding method. Six hours after seeding, 300μl media was added to each well.” It seems unrealistic that 5 μl of media would adequately support cell viability for 6 hours in a 48-well-plate well. Please clarify this and ensure a reproducible protocol.\nAnswer: The stated 5µl volume is correct and follows manufacturer recommendations for the spot seeding method (5-10µl until cells attach). To ensure a humidified atmosphere preventing evaporation of the cell culture medium, PBS was added to the space between the wells/measurement areas. We added this information to the revised method section.\nReviewer #5 (Remarks to the Author):\nIn this study, the authors investigate chamber-specific cis-regulatory elements (PID-CREs) that interact with their target genes using good quality Hi-C data. They identify PID-CREs that are specific to atrial and ventricular tissues and demonstrate their role in regulating chamber-specific marker genes by CRISPRi. Furthermore, the study highlights an enrichment of SNPs within these regulatory elements that are associated with arrhythmia-related GWAS signals, including those linked to long QT syndrome, thereby providing a valuable framework for investigating and functionally validating disease-associated genetic variants.\nThe authors have carefully and thoroughly addressed all of Reviewer 1’s comments. The revisions prompted by these comments have significantly strengthened the manuscript. Notably, the authors implemented several key improvements that enhance the robustness and functional relevance of their findings:\nAs recommended by Reviewer 1, the authors performed CRISPR interference (CRISPRi) targeting chamber-specific PID-CREs in atrial and ventricular hiPSC-derived cardiomyocytes (hiPSC-CMs), which led to changes in the expression of target genes.\nThey validated one of the CRISPRi results by performing complementary CRE deletion experiments, which yielded consistent outcomes.\nIn addition, the authors incorporated new functional data to support the impact of CRE perturbation in iPSC-CMs. Specifically, they conducted electrophysiological experiments, presented in Figure 8, which functionally validated two CREs at the electrophysiological level.\nIn their response, the authors also referenced recently published studies that corroborate their findings, particularly regarding the presence and relevance of PID-CREs.\nMinor comment:\nIn their CRISPRi experiments, the authors used atrial and ventricular cardiac differentiation hiPSC. It would be helpful to report the enrichment of atrial and ventricular cardiomyocytes obtained from each respective differentiation, to provide better context for the interpretation of the functional perturbation results.\nAnswer: Our differentiation protocol yields a high fraction of either ventricular or atrial hiPSC-CM with >90% cTNT+ CM after metabolic selection (new Supplementary Fig. 18). Sub-type specification efficiency was confirmed by flow cytometry using antibodies specific for MLC2a (MYL7, atrial) and MLC2v (MYL2, ventricular) as previously reported11. The atrial differentiation protocol achieved approximately 90% MLC2a+ CM. In comparison, the ventricular differentiation resulted in approximately 90% of MLC2v+ CM, highlighting the validity of our differentiation strategy. In addition, hiPSC-vCM showed a slight increase in MLC2a staining (17.7% MLC2a+/MLC2v+ hiPSC-vCM, new Supplementary Fig. 18). These results are in line with a previous report12. We further confirmed the quality of each differentiation visually based on the distinct morphology (Reviewer Fig. 4) and characteristic spontaneous beating frequencies of hiPSC-aCM and hiPSC-vCM12. To verify this quantitatively in an unbiased manner, we performed an RNA-seq analysis after the hiPSC-aCM and hiPSC-vCM differentiations used for the comparative analysis in Figs. 4 and 5. Both hiPSC-aCM and hiPSC-vCM display robust and specific expression of atrial and ventricular marker genes using the respective differentiation protocol. This includes the chamber-specific genes studied here. The ventricular CM marker genes KCNJ2 and HEY2 are 10- and 67-fold higher expressed in hiPSC-vCM vs. hiPSC-aCM. On the other hand, TBX5 and NPPA show the expected 4-fold higher expression in hiPSC-aCM as compared to hiPSC-vCM. We added this information to the results and method sections, as well as the new Supplementary Fig. 18.\nOverall, I find the authors’ responses to be adequate, and the revised manuscript acceptable for publication in its current form. I therefore recommend acceptance.\nAnswer: Thank you for the positive feedback and for recommending the manuscript for publication. We are especially grateful for reviewing the manuscript to replace the non-responding reviewer 1."}]

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