Engineered heart tissues facilitate functional characterization of noncoding variants implicated in Hypertrophic and Dilated Cardiomyopathy

Engineered heart tissues facilitate functional characterization of noncoding variants implicated in Hypertrophic and Dilated Cardiomyopathy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Engineered heart tissues facilitate functional characterization of noncoding variants implicated in Hypertrophic and Dilated Cardiomyopathy Zachary Weber, Tanner O. Monroe, Cory Holgren, Robert M. Mitchell, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7696371/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Inherited cardiomyopathies frequently arise from rare, highly penetrant coding variants with variable clinical expressivity. Recent biobank-scale genome-wide association studies (GWAS) suggest significant polygenic contributions to cardiovascular diseases, including cardiomyopathy. Most GWAS loci map to noncoding regions, which are poorly conserved across species, requiring a human genome context for experimental validation. Methods We created engineered heart tissues (EHTs) from human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes and primary cardiac fibroblasts. We assayed single-cell gene expression and chromatin accessibility to generate comprehensive genome-wide regulatory maps. Open chromatin regions (OCRs) were integrated with chromatin contact information and used to functionally fine-map single nucleotide polymorphisms (SNPs) in cardiomyopathy GWAS. SNPs and their associated regulatory regions were assessed using reporter assays, genome editing, and expression profiling. Results Single-cell RNA-seq of EHTs confirmed populations recapitulating major cell types found in hearts, with advanced cardiomyocyte maturation compared to monolayer hiPSC-cardiomyocytes. More than 400,000 OCRs were resolved to cell type and assayed for canonical transcription factor footprints. Functional fine-mapping of GWAS loci prioritized 5817 variants, and reporter assays on select variants validated allele-specific enhancer activity. We identified a locus harboring significant GWAS signals from both dilated cardiomyopathy and left ventricle ejection fraction in an intergenic region at chr3p25.1. Several of these variants lie in OCRs participating in long range chromatin interactions with SLC6A6 and GRIP2 . Haplotype-resolved and synthetic reporter assays confirmed enhancer activity and narrowed candidate SNPs. CRISPR-deletion of this region reduced expression of both SLC6A6 and GRIP2 , indicating the enhancer regulates the expression of more than one gene. Conclusions EHTs derived from hiPSCs are an experimentally tractable platform for testing the function of noncoding variants as modifiers of cardiomyopathy. Variants fine-mapped from cardiomyopathies using EHT regulatory maps have functional consequences and provide a set of prioritized sites to advance the study of polygenic heart failure liability. Epigenetics & Genomics Medical Genetics Cardiac & Cardiovascular Systems Engineered Heart Tissue Cardiomyopathy Functional Genomics Noncoding Variants Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Advances in human biobank genomics have dramatically increased our ability to identify candidate genetic risk variants for both common and rare diseases [1]. These conditions include cardiomyopathies –both dilated [2-6] and hypertrophic [2, 7, 8], as well as arrhythmogenic conditions such as Long QT Syndrome [9] and Brugada Syndrome [10, 11]. For example, two recent large-scale genome-wide association studies (GWAS) of dilated cardiomyopathy (DCM) report 80 and 70 risk loci respectively [5, 6], and one hypertrophic cardiomyopathy study (HCM) highlights at least 70 risk loci [8, 12]. These common variants generally map to regions noncoding regions, often distinct from monogenic genes for cardiomyopathy. The noncoding GWAS signals likely impart phenotypic effects through distal gene-enhancer interactions that regulate gene expression. The dozens of GWAS loci associated with both human HCM and DCM provide a much-expanded genomic context for variation contributing to these diseases. Nevertheless, it is challenging to identifying the regulatory variants that directly affect gene targets versus those that are simply linked to functional regions. The lack of conservation in noncoding regions across species renders assessment nontrivial in traditional model systems like mice. Human-specific myocardial disease models have largely relied on monoculture induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) [13, 14]. These models provide a critical experimental substrate, namely the complete human genome. Nonetheless, hiPSC-CMs are limited in both physiological and transcriptional maturity [15-17]. They also lack the cellular diversity present in developing and adult heart tissue, limiting the study of cell-type interactions and genetic pleiotropy. The development of three dimensional differentiated organoid cultures [18-20], and engineered tissues [21, 22] has progressively begun to mitigate concerns about maturation level and cell-type diversity to increase model fidelity and increase translational capacity in cardiac research. Here, we generated engineered heart tissues (EHTs) comprised of hiPSC-derived cardiomyocytes and primary human cardiac fibroblasts in a collagen-based scaffold in long term co-culture [23]. EHTs were interrogated using single-cell RNA and ATAC-sequencing, highlighting their enhanced maturity compared to hiPSC-CMs in 2D monolayer cultures. Using comprehensive regulatory maps generated from EHTs, 2D monolayer hiPSC-CMs, and ENCODE adult left ventricle ATAC-Seq [24, 25], we functionally fine-mapped 122 independent genomic loci from 12 GWAS cohorts of DCM and HCM. We identified thousands of prioritized variant candidates positioned to contribute to HCM and DCM, including their potential cell-type specificity. We validated several prioritized regions as enhancers, revealing haplotype-specific differences and links to specific effector genes, including SLC6A6 which encodes a taurine receptor and GRIP2 , encoding a protein that interacts with ephrin-B ligands. These data additionally provide a resource of epigenetic and expression data, along with functional fine-mapping results, to identify and test the effects of genetic variation on myocardial function. Methods Differentiation of human iPSC to Cardiomyocytes Ventricular hiPSC-CMs were generated by standard methods using small molecule modulation of WNT signaling [26, 27] adapted from previously published protocols. Briefly, when hiPSCs reached ~90% confluency, differentiation was initiated using 6mM CHIR99021(Tocris) for 24 hours in cardiomyocyte differentiation medium 3 (CDM3): RPMI 1640 (Thermo Fisher Scientific) supplemented with 2mM L-glutamine (Gibco), 213 mg/mL L-ascorbic acid 2-phosphate (Wako Chem), and 500mg/mL recombinant human albumin (Sigma). Media containing CHIR99201 was exchanged for fresh CDM3 at 24 hours. On day 3 cells were treated with 2mM Wnt-C59 (Tocris) in CDM3. On day 5, c59-containing media was changed for CDM3 and subsequently exchanged every two days until cells matured into beating monolayers. On day 10 of differentiation, beating cardiomyocytes were dissociated to single cells using 200units/mL collagenase IV (Worthington Biochemical Corporation) in Hank’s Balanced Salt Solution (Thermo) with 10mM HEPES, 2mM Thiazovivin (STEMCELL Technologies) and 30 mM N-Benzyl-p-Toluenesulfonamide (TCI) for 2 hours at 37°C. The single cell suspension of iPSC-CMs was filtered through a 100mm cell strainer and purified via PSC-Derived Cardiomyocyte Isolation Kit, human (Miltenyi Biotec), which routinely yields >90% purity, as assessed by flow cytometry from Cardiac Troponin T (BD Biosciences). Generation of Engineered Heart Tissues Engineered Heart Tissues were generated according to our previously published protocol [23], derived from the procedure originally outlined in by Tiburcy et al., [21, 22]. Briefly, we purified eGFP expressing hiPSC-CMs using column a column-based purification and combined them with human cardiac fibroblasts (Promocell) in a 9:1 ratio for a total of 0.55x10 6 cells per EHT in custom 48-well plates (Myriamed). Cells were suspended in RPMI containing B-27 supplement (Thermo Fisher Scientific) and combined over ice with 44 μL collagen type I (6.5 mg/mL, Millipore Sigma), 44 μL of 2× RPMI (Thermo Fisher Scientific), and 6 μL of 0.1N NaOH for a total of 198 μL per EHT, creating a hydrogel substrate mixture. 180 μL of the mixture was then transferred evenly into each well of the 48-well plate for EHT formation, resulting in a final cell density of 500,000 cells/well. Following the transfer, EHTs were incubated the 48-well plate at 37° C for 1 hour and before supplementing with EHT media containing DMEM low glucose (Millipore Sigma), 10% horse serum (heat inactivated, New Zealand origin, Gibco), 1% Penicillin/Streptomycin (Thermo Fisher Scientific), and 0.1% human insulin (10 mg/mL, Millipore Sigma). For 72 hours following EHT generation, the cells were supplemented with 5 μg/mL recombinant human TGF-β1 (CHO derived, PeproTech). On day 3 after tissue casting, TGF-β1 was removed from culture medium and media was exchanged with 500 μL fresh media per well. EHT media was exchanged every 2 days throughout the course of tissue maturation. EHTs were harvested for downstream applications at day 20 post tissue casting. Immunofluorescence Imaging of EHTs and 2D hIPSC-CMs For iPSC-CM monolayer imaging (2D), 3x10 5 cells were plated in 24‐well plate 12 mm on Matrigel‐coated micro cover glass slips (Electron Microscopy Sciences). Cells were washed with 30mM KCl in PBS and fixed with 10% formalin for 10 minutes, and then permeabilized with 0.2% Triton. They were then blocked for one hour with 10% donkey serum-PBS and then incubated overnight at 4°C with MYBPC3 Antibody (E-7, Santa Cruz) diluted 1:400 in 10% Donkey Serum-PBS, followed by a wash with 0.1% Tween-PBS. Next, they were incubated 1 hour at room temperature in 1:400 Anti-mouse Alexa 594 (ThermoFisher) in 10% donkey serum and then washed again with 0.1% Tween-PBS, then PBS. Nuclei were stained with DAPI (1:1000) in PBS, washed again with PBS, and then mounted onto glass slides with Prolong Gold (Thermo) and imaged on a Zeiss Axio Imager M2. EHTs were removed from the plates, washed with 30mM KCl in PBS, and fixed overnight in 10% formalin at 4°C. The EHTs were then washed in PBS and permeabilized with 0.5% Triton in PBS, three times for 30 minutes each time. They were then washed in PBS and equilibrated with 15% sucrose in PBS for 5 minutes, followed by 30% sucrose-PBS for 5 minutes. These equilibrated EHTs were then placed into Tissue-Tek® O.C.T. Compound (Sakura) and flash frozen in LiN2. Frozen EHTS were sectioned at 15µm onto glass slides. These sections were then blocked and stained using the same method as described for the 2D monolayers. Single-Cell RNA-Sequencing of EHTs To produce inputs for single cell transcriptomic datasets of EHTs, 2 x 48-well plates of EHTs were generated, representing two independent differentiations of input cardiomyocytes. The tissues were then pooled and incubated them at 37° C for 1-2 hours in a dissociation solution containing 1mg/ml Collagenase II (200 units/mg, Worthington),10 mM HEPES pH 7.4 (Gibco), 2 uM Thiazovivin (STEMCELL Technologies), and 30 uM N-Benzyl-p-Toluenesulfonamide, a contractility inhibitor, (TCI) to obtain a single cell suspension. A single cell RNA-sequencing library targeting 10,000 cells was prepared from this suspension using the 10X Genomics Chromium Platform at the NUSeq Core Facility at Northwestern University, Feinberg School of Medicine. We then repeated this procedure for a second time with two additional EHT differentiations to prepare an independent, replicated experiment. Both libraries were sequenced on the Illumina NovaSeq platform in 50bp paired-end format. Reads were aligned to the GRCh38.p13 transcriptome using the STAR [28] aligner as integrated into the 10X Genomics Cell Ranger count pipeline [29]. Cell x gene count matrices were then filtered on mitochondrial read content and total RNA content, as well as predicted doublet status, using Scrublet [30]. The datasets were then clustered, integrated, and analyzed using the Seurat package [31, 32] for the R programming language. Bulk RNA-Sequencing of 2D hiPSC-Cardiomyocytes Day 10 differentiated hiPSC cardiomyocytes were released from cell culture plates using trypsin-EDTA (Thermo Fisher Scientific), pelleted and washed in Dulbecco’s phosphate-buffered saline solution (DPBS) without calcium or magnesium (Thermo Fisher Scientific) and RNA was extracted using Qiagen RNeasy mini kit (Qiagen). Libraries were prepared using the NEBNext low input RNA library prep kit for Illumina (NEB) with NEBNext Illumina index primers and adapters. Sequencing of the RNA-Seq libraries was performed at the University of Chicago on an Illumina NovaSeq 6000 platform, and reads were aligned to the GRCh38/hg38 human transcriptome reference with NCBI refseq transcript models. Library strand orientation was confirmed using kallisto [33] and count matrices were generated by HTSeq-count [34]. Differential expression analyses were performed in R using the limma [35], and edgeR [36] packages. ATAC-Seq in EHTs and 2D hiPSC-CMs Day 30 EHTs were digested to a single cell suspension as described above and sent for fluorescence-activated cell sorting (FACS), gating on expression of eGFP. eGFP- fibroblasts were separated from the eGFP+ iPSC-CMs, producing CM enriched (CM+) and fibroblast enriched (FB+) cell pools. The sorted cells were washed again with 10mL EHT media and resuspended in 1mL with 2%BSA. The cells were then counted and separated into aliquots of 50,000. Assay for transposase-accessible chromatin sequencing (ATAC-Seq) was performed as described by Grandi, F. C. et al. [37]. Briefly, 50k cells were collected at 500g for 5min at 4°C and gently resuspended in 50µl ice-cold lysis buffer: 10 mM Tris–HCl pH 7.5, 10 mM NaCl, 3 mM MgCl2, 0.1% NP40 (Sigma), 0.1% Tween-20 (Thermo Fisher Scientific), and 0.01% digitonin (Promega). Lysis proceeded for 3min on ice and was neutralized with 1ml of ice-cold lysis buffer excluding detergents digitonin and NP40. Following neutralization, permeabilized nuclei were pelleted at 4°C and resuspended in 50µl transposition master mix: 25µl (2X) Illumina TD buffer, 16.5µl PBS, 5µl H2O, 0.5µl 1% digitonin, 0.5µl 10% Tween-20, and 2.5µl Illumina TDE1 enzyme (Illumina), and incubated for 30min @ 37°C on a thermomixer. Tagmentation was terminated and DNA collected using Zymo Clean and Concentrator-5 (Zymo) following the manufacturers recommendations. Libraries were barcoded with 5 cycles of preamplification, and the PCR reaction was paused for quantification using NEBNext Library Quant Kit for Illumina (New England Biosciences). Diluted final libraries were mixed and sequenced on an Illumina Nova-Seq 6000 using paired end 50bp reads with a target depth of ~300M reads/sample. Detailed descriptions of read processing, peak calling, and downstream analyses of ATAC-Seq libraries can be found in the Detailed Methods section of the supplement. Effect Size Estimates and Functionally informed Fine-mapping Summary statistics for fine-mapped traits ( Supplemental Table 1 ) were lifted over to GRCh38.p13/hg38 in R v.4.1.0 using the liftOver package (https://doi.org/10.18129/B9.bioc.liftOver). Effect-size enrichment estimates and SNP-level prior weights were obtained in each dataset using TORUS [38], where loci were split into 1600 distinct LD blocks across the human genome for estimation. The prior weights were then utilized in functionally informed fine-mapping in R with the susieR [39] and mapgen [40] packages to obtain estimates of posterior inclusion probabilities at each locus in both HCM and DCM. The estimates were compared with uniform prior fine-mapping in the same tools, and loci were prioritized for further study based on their apparent localization to distal regulatory elements and the exclusion of high probability coding variants in the credible sets. Luciferase Assays Genomic sequences used in luciferase reporter assays for fine-mapping at PRKCA, VTI1A / TCF7L2, and the GATA4 locus were designed as 1000-bp sequences centered on the variant of interest ( Supplemental Table 2 ), with an additional 5’ homology sequence (GGCCTAACTGGCCGgtacctgagctcgctagcctcga) and 3’ homology sequence (atcaagatctggcctcggcggccaaGCTTAGACACTA) for Gibson assembly into the Promega pgL4.23[ luc2 /minP] backbone. These sequences were synthesized as linear IDT gBlocks. The backbone was propagated in NEB Stable Competent E. coli, linearized by XhoI and EcoRV (NEB), and assembled on a thermocycler using NEB Gibson Master Mix. Constructs were dialyzed in deionized water using Millipore MF membrane filters and transformed into NEB stable E coli for propagation, isolation, and sequencing. Longer haplotype sequences for luciferase assays of the chr3p25.1 enhancer ( Supplemental Table 2 ) were synthesized and delivered as complete plasmids by VectorBuilder, and were similarly propagated, isolated, and sequenced before transfection into hiPSC-CMs. For transfection and activity readings, hiPSCs were differentiated to cardiomyocytes using methods described above, isolated at day 10 using column purification, then plated at 30,000 cells per well into clear bottomed, opaque-walled 96-well plates designed for use with Promega plate-readers (Corning costar). Cells were maintained in RPMI 1640 supplemented with B27 and L-Glutamine. After 24 hours, media was exchanged for media containing 2 uM CHIR99201 and was replaced every 48 hours for the duration of the culture. When cells reached 90% confluency, they were co-transfected with experimental constructs and Renilla normalization controls (Promega) into cells. 20 hours-post transfection, cells were washed, and media was replaced with CHIR99201 containing media. At 48-hours post transfection, cells were lysed, and luciferase activity was read using Promega Glowmax Multi-detection system and Promega Dual-Luciferase Reporter Assay System kit. CRISPR Editing of hiPSCs To obtain hiPSC cell lines with one or more copies of the chr3p25.1 enhancer region removed, we made use of the IDT CRISPR Alt-R genome editing system. Briefly, we obtained four custom crRNAs targeting each of the flanks of the enhancer region ( Supplemental Table 2 ) and complexed them with Alt-R tracrRNAs (IDT) and Alt-R S.p. Cas9 V3 (IDT) to form four distinct editing ribonuclear proteins (RNPs). These RNPs were pooled in equal quantity and nucleofected into approximately 1x10 6 hiPSCs per manufacturers’ recommendations using the Lonza 4D Nucleofector System and the Amaxa P3 Primary Cell X Kit L (Lonza). After nucleofection, cells were plated in a single well of a 6-well plate with media containing ROCK inhibitor. Nucleofected cells were left to recover for 48 hours then sorted using FACS at one cell/well into Matrigel coated 96-well plates. Cell colonies were then expanded and passaged until enough material for genotyping could be collected. To genotype edited cells, cell scrapes were taken from 24-well plates and DNA was extracted using Lucigen QuickExtract Buffer (Biosearch Technologies). The genotype of the cells was assayed using PCR with custom primers spanning the target deletion breakpoints, as well as internal primers used to distinguish heterozygous cells. Selected cells were further expanded and banked. Their genotypes were confirmed via additional DNA isolations of larger cell pellets using the Promega Wizard Genomic DNA purification kit, PCR, and Sanger sequencing. Results Generation of Engineered Heart Tissues as a Model of Human Myocardium Although monolayer hiPSC-CM cultures have revolutionized cardiac disease research [13, 14], their cellular immaturity limits their potential as an experimental system. Engineered heart tissue or engineered heart myocardium [18-22] systems have been developed by mixing hiPSC-CMs with fibroblasts and other cell types, and then placing cell mixtures onto bio-scaffolds where they mature under tension. There are multiple formats for tissue generation, varying in bio-scaffold content and form, ranging from simple strips to rings or even bio-printed ventricular shapes [41]. Here, we molecularly characterized the transcriptomic and open chromatin landscapes of human EHTs and compared these data to hiPSC-CMs in 2D cultures and to left ventricle (LV) myocardium. To generate EHTs, eGFP-expressing human iPSCs were differentiated to cardiomyocytes in monolayer cell culture for 10 days using WNT modulation followed by depletion of stem cells and enrichment for cardiomyocytes [21-23, 42]. Enriched cardiomyocytes were combined 9:1 with fibroblasts isolated from human hearts in a three-dimensional collagen-based scaffold, forming approximately 15mm ring-like EHTs around two posts. The tissues were then matured for an additional 20 days, totaling 30 days from hiPSC-CM induction to mature EHTs ( Figure 1A ). Immunofluorescence imaging with an antibody to cMyBP-C in EHTs revealed cardiomyocyte alignment as well as sarcomere alignment parallel to the axis of contraction ( Figure 1B ). By comparison, 2D monolayer hiPSC-CMs show sarcomeres radially aligned around central nuclei, with little cell-cell alignment ( Figure 1C ). These characteristics agree with previous morphology described in EHTs [42-44], and further suggest that this cellular alignment more closely models in vivo cardiomyocytes. Single-cell gene expression in EHTs To assess the cell-type composition and gene expression patterns within EHTs, we dissociated pools of day 30 EHTs, collected from 2 independent differentiations, into single cell suspensions, and generated single-cell RNA seq libraries using the 10x Genomics Chromium TM platform. After sequencing, quality control, and filtering, we obtained 17,081 cells. Unbiased clustering of the transcriptomic data revealed 14 communities ( Supplement 1A-1C ) comprising six distinct cell-types ( Figure 1D ). By cell number, cardiomyocytes (42.4 %) and cardiac fibroblasts (37.9 %) represented most of the tissue. This cardiomyocyte percentage is similar to estimates found in human hearts from the Heart Cell Atlas (HCA) project, where cardiomyocytes constitute 49% of ventricle cells [45]. SMC/pericyte-like, mesothelial progenitors, neural-like, and endothelial cells contribute the remaining 19.7% of the cell population. EHT cardiomyocytes expressed genes for canonical sarcomere components and known cardiac transcription factors ( Figure 1E, Supplement 1D ). Furthermore, average expression of the adult ventricular myosin heavy chain, MYH7 , exceeded that of the fetal isoform MYH6 in EHTs. In general, each major cell-type expressed canonical markers such as FN1 in cardiac fibroblasts, RGS5 in pericyte/SMC cells, PECAM1 in endothelial cells, and SOX2 in neural-like cells ( Figure 1F ). As a validation of these cell type annotations, we compared our labels with predictions from CellTypist [46, 47] models trained on the human Heart Cell Atlas. We found general agreement in the predictions of the major cell types, adding confidence regarding EHT composition ( Supplement 1B ). To assess potential heterogeneity within the EHT-CM population, we subclustered EHT-CMs at higher resolution with particular attention to sarcomere and transcription factor gene expression. This analysis revealed populations spanning the maturation spectrum ( Figure 2A ), aligning well with the second principal component of variation in the expression data ( Figure 2B ). We noted a continuum of early, still cycling, and late cardiomyocytes, characterized by differential regulation of canonical adult and neonatal sarcomere genes as well as the specific transitions between MYH6 and MYH7 (Figure 2C). We also noted subsets of cardiomyocytes across maturation states expressing extracellular matrix components (noted as ECM+). We identified a cardiac progenitor population defined by high expression of the transcription factors GATA4 , TBX20 , and MYOCD ( Figure 2C ). To compare cardiac gene expression between input hiPSC-CMs and EHT-CMs, we performed RNA-seq on 2D hiPSC-CMs. Gene expression was moderately and significantly correlated (r: 0.476334. P-value: 0.0025) between the datasets ( Figure 2D ). However, several genes characteristic of mature cardiomyocytes were expressed at substantially higher levels in EHTs, including the sarcomere components TPM1 , ACTC1 , TTN , MYL2 , MYL3 , TNNC1 , and MYH7 . Conversely, we observe only one gene relatively upregulated in 2D hiPSC-CMs, MYH6 , the MYH isoform that characterizes the developing ventricle, highlighting a difference in maturity between the systems. Open chromatin profiling of engineered heart tissues Given the advanced maturation of gene expression patterns in EHT-CMs relative to 2D hiPSC-CMs, we speculated that the enhancer landscape of myocardial genes may also more closely resemble that of the adult heart. To test this hypothesis, we generated deep ATAC-Seq datasets from day 30 EHTs and 2D hiPSC-CMs. To separate cell populations into cardiomyocyte-enriched (CM) and fibroblast enriched (FB) pools from which to generate libraries, we sorted the input cell populations using FACS, gated for eGFP signal (Figure 3A) . For an adult myocardial comparison, we also reprocessed sequencing data from eight libraries of four high quality bio samples taken from left ventricle (LV) tissue originally published for the ENCODE project [24, 25] ( Supplemental Table 1 ). After peak calling and filtering, this analysis produced a set of 160,270 fixed-width open chromatin regions (OCRs) in the reference ENCODE LV dataset and 153,189 OCRs in the 2D hiPSC-CM ATAC-Seq ( Figure 3B ). In the EHT-CM and EHT-FB datasets, we found substantially more OCRs: 221,530 and 185,303 regions respectively, for a combined dataset of 406,833 OCRs. This increased number of OCRs likely reflects the model, which includes developmental and adult gene expression patterns, further enhanced by the uniquely deep sequencing applied to the EHTs. The pattern of genomic context of the identified OCRs was consistent with what is expected from ATAC sequencing [48], where the plurality of regions mapped to introns, followed by intergenic regions, promoters, exons, and finally transcription termination sites ( Figure 3C ). Comparing the EHT ATAC-Seq results to ENCODE LV data reprocessed using the same metrics, we detected 206,077 EHT intersecting at least one element in the ENCODE LV set ( Figure 3D ). Of the EHT-CM OCRs, we also found 130,571 annotations (58.9%) overlapped at least one region in the 2D hiPSC-CM set; demonstrating broad overlap with both the adult and early states. We illustrate one example proximal to the cardiac troponin C1 gene, TNNC1 , which is absent in ENCODE LV but found in both EHT and 2D hiPSC-CMs ( Figure 3E, arrow ). We also observe many sites where the EHT dataset mimics a hybrid state between the early open chromatin structure of 2D hiPSC-CMs and the mature structure of ENCODE LV tissue. This hybrid state is exemplified at the MYH6 - MYH7 locus where 2D hiPSC-CMs display strong open chromatin signal at a proximal promoter element of the fetal ventricular heavy chain MYH6 ( Figure 3F, arrows ). In contrast, the adult LV shows open chromatin approximately 1900 bp upstream, with diminished signal at the proximal promoter.The EHT CM OCR dataset captures both elements with comparable signal strength. These findings suggest EHTs can model genomic perturbations relevant to cardiac development and the more mature myocardium. As an additional validation of the cardiac regulatory elements, we analyzed transcription factor binding sites within EHT CM OCRs. We found canonical cardiac transcription factors motifs among the most enriched in the EHT OCRs ( Figure 3G ) including those of MEF2C, GATA4, TBX20, NKX2-5 , and TBX5. CM- and FB-enriched ATAC-Seq data also enable deconvolution of the bulk ENCODE LV signal into relative cell type contributions. For instance, at DDR2 , a gene encoding a collagen-binding receptor tyrosine kinase involved in fibrosis, we detect several promoter elements as well as distal intronic OCRs in the fibroblasts rather than the cardiomyocytes ( Figure 3H, arrows ). Motif enrichment within fibroblast footprints suggests an active state, likely reflecting the TGFb supplementation used during the tissue consolidation process. These include motifs corresponding to TCF1, ETS1, FLI1, and NR2F2 ( Figure 3I ). These fibroblast-specific sites provide an additional opportunity to assess potential fibroblast contributions to cardiac disease. Functional Fine-mapping of Cardiomyopathies from EHTs The depth of OCR annotations in EHTs enabled fine-mapping of cardiomyopathy GWAS loci [2, 5-8]. Functionally informed fine-mapping is a two-step, empirical Bayes approach to variant prioritization that uses genomic annotations to estimate how strongly GWAS effect sizes are enriched in specific functional regions. These enrichment estimates are then used as prior probabilities and combined with linkage disequilibrium data to pinpoint variants likely to contribute to trait biology at the locus (Figure 4A) . We generated effect-size enrichment estimates using TORUS [38] for each OCR dataset, and applied this analysis to EHT-CM, EHT-FB, iPSC-CM, and ENCODE LV data. We used summary statistics from a multi-trait GWAS (MTAG) conducted on cohorts with hypertrophic cardiomyopathy [8] and dilated cardiomyopathy [6], left ventricle ejection fraction (LVEF) and left ventricle end-diastolic volume (LVEDVi) [49]. We also selected two traits unrelated to myocardial tissue: body mass index (BMI) [50] and schizophrenia (SCH) [51] to serve as trait controls. Our estimates reveal strong GWAS effect size enrichment in all cardiac OCRs across each of the cardiac traits ( Figure 4B ). Both EHT-CM and ENCODE LV OCRs were enriched for variants associated with contractile traits like LVEF and LVEDVi, as well as DCM and HCM, reflecting the contributions of cardiomyocytes and fibroblasts underlying those phenotypes. By comparison, unrelated traits of body mass index and schizophrenia showed no enrichment ( Figure 4B ). The strong enrichment in cardiomyopathy associated GWAS effects may reflect increased maturation of gene regulatory circuits in EHTs across entire pathways, relative to 2D hiPSC-CMs. Assaying pathway-level gene expression in EHT-CMs, we note higher expression in EHT-CMs in the sarcomere, as well as in cardiomyopathy and several other disease-associated gene sets, and sets related to cardiac development and contractile function ( Figure 4C ). Taken together, these estimates demonstrate that the open chromatin regions in EHTs are globally enriched for SNPs identified in GWAS of cardiac traits at levels comparable to adult left ventricle. We next used the enrichment estimates to derive SNP-level prior probabilities for statistical fine-mapping of each GWAS and OCR dataset with SuSieR [39, 52]. SuSieR uses summary statistics data from GWAS studies and linkage disequilibrium data (LD) to perform fine-mapping. At each locus, we obtained a group of SNPs that together have a 90% chance of containing a functional variant, termed a “credible set.” Each SNP was then assigned a posterior inclusion probability (PIP), reflecting the probability that the variant is functional. In total, 5817 SNPs were mapped to at least one credible SNP set in HCM (2545 SNPs across 83 loci) and/or DCM (3870 SNPs across 87 loci) ( Supplemental Tables 3-5 ). We found that average credible set sizes were reduced using this prioritized fine-mapping compared to uniform fine-mapping of the same loci. EHT-CM fine-mapping especially outperforms the other analyses in reducing large credible sets of 30 or more SNPs ( Supplement 2A ). Most fine-mapped SNPs were selected into more than one credible set across each OCR input, indicating a measure of robustness in the strategy ( Supplement 2B ). These SNPs included in multiple credible sets tended to show higher PIPs when using OCR prior probabilities (max functional PIP) when compared to fine-mapping with uniform priors (uniform PIP) ( Figure 4D ). This finding suggests that SNPs within open chromatin regions are more likely to be prioritized as potentially functional, as opposed to simply being in LD. A substantial proportion of the entire set of 5817 SNPs were found to intersect with at least one OCR dataset (17.3%), with DCM SNPs in OCRs at a slightly higher rate (20.5%) than HCM SNPs (15.7%) ( Supplement 2C ). Most of this overlap could be reproduced in EHT datasets alone (Combined: 13.6%, HCM: 12.4%, DCM 16.2%). These fine-mapping results allowed us to identify individual variants of interest which are likely to be functionally relevant for cardiomyopathy expression, improving the efficiency of downstream variant-to-function analysis. Interrogation of Noncoding Variants in HCM and DCM When fine mapping the HCM and DCM GWAS data, we found that most credible set SNPs mapped within noncoding regions of the genome, including those SNPs identified as having the highest probability of being functional. The annotation indicates that these SNPs are likely to modify gene expression via cis -regulatory mechanisms, as has been suggested for HCM and DCM associated GWAS signals [53]. To further prioritize loci potentially acting via cis-regulatory mechanisms, we intersected many these credible sets with chromatin conformation capture data which we previously generated in hiPSC-CMs [27]. We identified four HCM GWAS variants at chr17q24.2 that were strongly prioritized by fine-mapping. These SNPs, rs17633401, rs67700546, rs12601850, and rs9892651, were in a locus spanning the PRKCA gene, which encodes protein kinase C alpha ( Figure 5A & Supplement 3A ). Both rs67700546 and rs17633401 were in OCRs shared by EHT-CMs and EHT-FBs, while rs12601850 intersects a fibroblast specific element, and rs9892651 did not intersect an OCR in any dataset ( Figure 5A ). In enhancer assays in hiPSC-CM, the regions displayed activity, with rs12601850 and rs9892651 conferring allele-specific expression effects ( Figure 5B) . These variants were also identified in GTEx [54] as eQTLs for expression of PRKCA in left ventricle in the expected direction, adding external support to the presence of one or more functional variants at the locus( Figure 5C ). These results illustrate that the genetic architecture of a given GWAS-associated locus may involve more than one functionally relevant variant affecting different enhancers [55, 56], potentially in different cell types. We fine-mapped an additional HCM signal within an intron of VTI1A , a gene implicated in vesicle transport on chr10 at 10q25.2 ( Supplement 4A ). The credible set for this signal spans approximately 38 kilobases (kb), with both ends forming distal chromatin interactions with the TCF7L2 promoter approximately 245 kb away. Interestingly, the EHT-CM and ENCODE LV fine-mapping prioritized two different variants; rs7096151 (EHT CM PIP = 0.878) and rs10885378 (ENCODE LV PIP = 0.542), which may be due to the structure of the underlying open chromatin data. rs7096151 lies in an OCR found in EHT-CM and 2D hiPSC-CMs. Conversely, rs10885378 is at the edge of an ENCODE LV OCR ( Supplement 4A ). To test whether either variant was located in cardiac enhancers, we expressed 1000-bp sequences centered on each SNP in hiPSC-CMs to assay luciferase reporter activity. rs10885378-T showed enhancer activity, while rs10885378-C was not significantly different from gene desert or rs10885378-T, indicating modest genotype-specificity ( Supplement 4B ). Similarly, rs70961651-A trended towards higher enhancer activity than rs70961651-G, but was not significantly different (P=0.09) ( Supplement 4B ). The transcriptional regulator TCF7L2 is differentially active in failed hearts [57], thus, it is possible that this SNP might demonstrate stronger effects under conditions of heart failure, or that both regions may contribute and act synergistically. We also dissected a credible set associated with DCM locus at chr8p23.1. The credible set at this locus spans 30 kb and forms long-range chromatin interactions over 280 kb in hiPSC-CM with the developmental cardiac transcription factor gene, GATA4 ( Supplement 5A ). A single variant, rs13260325, strongly prioritized in EHT-CM fine-mapping (PIP = 0.46), ENCODE LV (PIP = 0.577) and 2D-hiPSC-CM (PIP = 0.436), overlapped an OCR shared in all datasets ( Supplement 5A ). When placed into a luciferase reporter and transfected into hiPSC-CMs, rs13260325 acts as an enhancer of luciferase gene expression ( Supplement 5B ). These data suggest that noncoding variants, albeit at a distance from GATA4 , may impart their effects on through cis -regulation of GATA4 expression in addition to the established roles of rare coding variants in GATA4 in DCM [58]. Characterization of the 3p25.1 HCM Locus We identified a DCM-associated region at chr3p25.1 harboring a GWAS signal concentrated in an intergenic region near the LSM3 gene previously highlighted in Garnier et al, 2021 [3]. Fine-mapping of the locus prioritized a credible set spanning 6 kb overlapping a dense set of OCRs shared in multiple cardiac data sets ( Figure 6A ). This cluster of OCRs forms long-range chromatin interactions with SLC6A6, and GRIP2. Each fine-mapping approach strongly prioritized SNP rs6807275 (EHT-CM PIP = 0.886). Variants in this DCM-chr3p25.1 credible set are arranged into four predominant haplotypes in European-associated ancestry populations; designated haplotypes ‘A-D’. Haplotypes ‘A’ and ‘B’ occur most frequently (Allele frequencies: A = 0.455; B = 0.240) (Figure 6B) . Both haplotypes carry the prioritized SNP rs6807275, but differ in three other linked variants (rs6786141, rs7650762, and rs12634565) common to the ‘C-D’ group sequences. Luciferase reporter assays confirmed enhancer activity of each of the four haplotypes expression with levels significantly higher than our negative control non-enhancer sequence (A; P = 9.19x10 -8 , B; P = 6.80x10 -10 , C; P = 1.38x10 -6 , D; P = 6.96x10 -6 ) ( Figure 6C ). Furthermore, we observed that the ‘A’ haplotype – which is associated increased DCM risk, showed the lowest average enhancer activity, and differed significantly from the ‘B’ haplotype (P=6.72x10 -3 ). We hypothesized that one or more of the SNPs differing between ‘A’ and ‘B’ conferred cis -compensatory, stabilizing enhancer function to mitigate the effect of rs6807275. To test this hypothesis, we created synthetic haplotypes on the ‘A’ background, introducing the single substitutions of each ‘B’ specific variant and similarly tested these sequences in hiPSC-CMs ( Figure 6D ). Two of the tested B-Haplotype SNPs, rs6786141 (P =2.29x10 -2 ) and rs7650762 (P =2.25x10 -2 ) restored enhancer activity, consistent with a compensatory effect ( Figure 6E ). Notably, both SNPs are also shared on the two minor ‘C-D’ group haplotypes. Reporter constructs for these haplotypes exhibited intermediate activity relative to A and B, suggesting that regulatory output at this locus reflects combinatorial interactions between at the three variants, highlighting a complex cis-regulatory architecture centered on rs6807275. Finally, we deleted a 6 kb region containing the enhancer haplotypes in hiPSCs using CRISPR-Cas9 genome editing to test its regulatory influence on gene expression, generating heterozygous and homozygous knockout lines. We then differentiated these hiPSCs to CMs and assessed the transcriptome by RNA-seq (Figure 6F) . In enhancer deleted hiPSC-CMs, the expression of both SLC6A6 (P=9.27x10 -4 )and GRIP2 (P=1.77x10 -2 ) was significantly reduced ( Figure 6G-H ). Despite being highly expressed in the heart, GRIP2 has not been well studied for its cardiac role. There is evidence for role of the taurine transporter SLC6A6 in cardiomyopathy [2, 3, 59], and our data indicates this genomic region regulates both genes and the possibility that both genes contribute to trait liability at the locus. Together, these results identify the GWAS signal at chr3p25.1 as localizing to a distal enhancer, positioned as a modulator of SLC6A6 and GRIP2 expression. These data indicate multiple variants within a single region can induce changes in expression of more than one target gene and may have broader impacts outside of individual-trait biology. Discussion The genetic understanding of both hypertrophic and dilated cardiomyopathy is rapidly expanding to include significant contributions of common, noncoding variation in the genome. In the last five years, the number of common-variant associated loci have quadrupled from approximately 30 to more than 120 across the two conditions. Consequently, the development of comprehensive functional annotations in cardiac cells coupled with functional genomic analysis carried out in human models of myocardium are increasingly important to bridge the gap between genetic inference and cell biological mechanisms. In this work, we used human EHTs to provide a model of human myocardium that is cellularly diverse, and more morphologically and transcriptionally mature than 2D hiPSC-CMs. Because EHTs are derived from human IPSCs, they carry the human genome and are well suited to examine noncoding variation that may not be well conserved across species, yet relevant to the expression of human cardiac traits. Although not fully mature, the intermediate maturation state of EHTs yields an experimental system with broad flexibility as a platform for human cardiac genetics since it can be used to study both developmental and adult-onset conditions. This is illustrated in the functional fine-mapping of GWAS traits, where those traits can have early and late effects mediated by developmentally important genes, genes responsible for long-term maintenance of function, or environmentally responsive genes. We showed that the open chromatin regions from EHT datasets could be integrated into statistical fine-mapping procedures to help concentrate evidence on smaller credible set of genomic variants and narrow downstream priorities for further study. At the same time, EHTs can provide insight into which cell-type components may be more likely to contribute effects on a single-variant basis. Through this work, we generated unique datasets that add to existing data derived from human hearts. Combined with statistical fine-mapping tools, this integrated information provides a roadmap for examining genetic variation in noncoding regions that may not have comparable regions in model systems like mice. We found that many GWAS variants located to active cardiac enhancers and that those variants can have allele-specific effects. This data provides experimental evidence for the role of noncoding cis -regulatory contributions to genetic cardiomyopathy liability. Moreover, this risk is not restricted to genetically mediated cardiomyopathies as these nocoding regions may influence cardiac function in a variety of heart failure etiologies. Testing a subset of variants identified using this approach, we demonstrate the functional consequence of specific regulatory elements, supporting their contribution to modifying gene expression. Illustrating the power of a combination of refined genomic annotations in EHTs with statistical methods for fine-mapping loci associated with cardiomyopathies, we demonstrate a specific distal enhancer-gene interaction at chr3p25.1 between an upstream regulatory element localized to a DCM GWAS signal. Although this region falls closest to LSM3 , the 3D chromatin mapping data link this region to a region containing the SLC6A6 and GRIP2 genes, both of which are expressed in human myocardium. SLC6A6 encodes the taurine receptor, and loss of function variants in SLC6A6 cause a potentially treatable form of cardiomyopathy associated with low taurine content [ 59 ]. Taurine supplementation has been studied as a in people with heart failure and cardiomyopathy [ 60 ], however, these studies were not conducted with consideration of underlying genotypes that influence expression of its receptor. Notably, this region was previously identified in GWAS for DCM [ 3 ], and it was suggested this region likely regulated SLC6AC . However, we demonstrated that deleting this region from chr3p25.1 markedly reduced both SLC6AC and GRIP2 , indicating this region regulates more than one gene. GRIP2 is enriched in cardiac expression and encodes an ephrin B1 interacting protein [ 61 ], and ephrin B1 is implicated in heart development and function [ 62 ]. This example demonstrates how the fine-mapping and refined analysis, combined with functional genomic annotations in EHTs, can serve as a critical ancillary tool to link genetic variation associated with cardiomyopathies with genes and their function. Additionally, the EHT OCR dataset generates a major addition to previously available data. Using these datasets, we can detect previously unannotated open chromatin signals, capture temporal or transient regulatory dynamics in cardiac biology, open avenues for study in fibroblast specific regulation of cardiac traits, and leverage powerful functional fine-mapping paradigms to increase the efficiency of discover from genome-wide association studies. Finally, this work provides a wealth of new, publicly available resources broadly applicable to cardiac disease genetics. Our comprehensive OCR datasets add substantially to known open chromatin annotations of hiPSC-differentiated cardiomyocytes and primary cardiac fibroblasts. Additionally, our fine-mapping results serve as an anchor point for a wide variety of downstream assays and avenues of future study. Abbreviations ATAC-seq Assay for Transposase-Accessible Chromatin Sequencing CM Cardiomyocyte DCM Dilated Cardiomyopathy ECM Extracellular Matrix EHT Engineered Heart Tissue FACS Fluorescence-Activated Cell Sorting FB Fibroblast HCM Hypertrophic Cardiomyopathy hiPSC Human Induced Pluripotent Stem Cells hiPSC-CM Human Induced Pluripotent Stem Cell Derived Cardiomyocyte GWAS Genome Wide Association Study OCR Open Chromatin Region SNP Single Nucleotide Polymorphism Declarations Acknowledgments: This research was supported in part through contributions provided by the Genomics Compute Cluster which is jointly supported by the Feinberg School of Medicine, the Center for Genetic Medicine, and Feinberg's Department of Biochemistry and Molecular Genetics, the Office of the Provost, the Office for Research, and Northwestern Information Technology. The Genomics Compute Cluster is part of Quest, Northwestern University's high performance computing facility, with the purpose to advance research in genomics. We thank the Robert H. Lurie Comprehensive Cancer Center of Northwestern University in Chicago, IL, for the use of the Flow Cytometry Core Facility, which provided cell sorting services. Sources of Funding: This work is supported by NIH HL128075 (EMM), NIH HL168239 (TOM), HL131914 (EMM), CA060553 (Flow cytometry core); American Heart Association Strategically Focused Research Network on Arrhythmia and Sudden Cardiac Death; Leducq TransAtlantic Foundation. Disclosures: EMM consults for PepGen, Tenaya Therapeutics, Novartis, and is a founder of Ikaika Therapeutics and Kardigan. These activities are unrelated to the content of this manuscript. References Wang, Q., et al., Rare variant contribution to human disease in 281,104 UK Biobank exomes. Nature, 2021. 597 (7877): p. 527-532. 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Nóbrega","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYFACHjApB2IcgAoZEKXFmHQtiQ1QBmEt5uy9xz58+GWXvuH42YMHftTUyjGwN2+TwKfFsudc8syZfcm5G87kJRzsOXbcmIHnWBleLQY3coyZeXuYczfc4DE4zMB2LLFBIseMsJa/PfXpBmAt/4Ba5N8QoYXhx+EEsBbGthqgLTwEtJw5l8zY23DccOaZHIODvX0HjNl40oot8Go53nuY4cefanm+42eMP/z4VifHz3544w18WsCAsQ3OBIYAQeVg8AfOqiNOwygYBaNgFIwoAAD0g05yIUVGzwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Chicago","correspondingAuthor":true,"prefix":"","firstName":"Marcelo","middleName":"A.","lastName":"Nóbrega","suffix":""}],"badges":[],"createdAt":"2025-09-23 16:16:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7696371/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7696371/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92134005,"identity":"55c88868-482a-4c54-af69-c5bcb7895b20","added_by":"auto","created_at":"2025-09-25 03:58:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":399016,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptText.docx","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/c332d31e0ef8e7074210741e.docx"},{"id":92134197,"identity":"788bd965-8ef1-46cf-8daf-faf3616aee55","added_by":"auto","created_at":"2025-09-25 04:06:08","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs7696371.json","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/90f92a36624ba336b5fd64c5.json"},{"id":92134007,"identity":"f5ebfedb-ff78-492e-ab14-5110b4b7e03c","added_by":"auto","created_at":"2025-09-25 03:58:08","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130870,"visible":true,"origin":"","legend":"","description":"","filename":"rs76963710enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/b5e39b25b56e600a308dfc4b.xml"},{"id":92134199,"identity":"5621003d-473a-4812-bcfb-707d3e47f40a","added_by":"auto","created_at":"2025-09-25 04:06:08","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129274,"visible":true,"origin":"","legend":"","description":"","filename":"rs76963710structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/91cf8f36c115dc3ed7773365.xml"},{"id":92134015,"identity":"842fd0ae-8b42-4e24-952f-7b0609d20391","added_by":"auto","created_at":"2025-09-25 03:58:08","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140212,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/8b33e684893ad94c8f6c7d45.html"},{"id":92134002,"identity":"f9250e62-c229-494a-bb32-580e5e870fbf","added_by":"auto","created_at":"2025-09-25 03:58:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1530279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneration of Engineered Heart Tissues and Single-Cell Profiling.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Overview of the differentiation and generation of Engineered Heart Tissues (EHTs) from hiPSCs and primary cardiac fibroblasts with representative photo of day 30 EHT. \u003cstrong\u003eB.\u003c/strong\u003e Immunofluorescence microscopy (IFM) image of EHT section stained for with an antibody to MyBP-C (red) for sarcomere visualization, and DAPI (blue) for visualization of the nucleus. \u003cstrong\u003eC. \u003c/strong\u003eIFM image of 2D hiPSC-CM stained for cMyBP-C (red) and DAPI (blue). \u003cstrong\u003eD. \u003c/strong\u003eUMAP Projection of single-cell RNA-Seq performed on EHTs demonstrating clustering, colored by cell-type groups. \u003cstrong\u003eE. \u003c/strong\u003eCanonical marker expression across cell type groups in EHT. Circle size indicates percent of cells in the cluster expressing the marker, while color indicates the average expression within cells. \u003cstrong\u003eF. \u003c/strong\u003eUMAP projections of select cell type markers showing restricted expression in the EHT.\u003c/p\u003e","description":"","filename":"Figure1ZWR.png","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/5c7593985dbc982cce3edcf5.png"},{"id":92134852,"identity":"d49d96f7-22c0-4dc4-85e3-cfae40599fbd","added_by":"auto","created_at":"2025-09-25 04:14:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":211497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Expression in EHT Cardiomyocytes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eUMAP projection of EHT cardiomyocytes re-clustered to assess granular state-level structure, colored by annotated subpopulations. \u003cstrong\u003eB. \u003c/strong\u003eViolin plot of the second principal component of expression for EHT cardiomyocyte subclusters illustrating trajectory of states from progenitor to late cardiomyocyte. \u003cstrong\u003eC. \u003c/strong\u003eGene expression in EHT cardiomyocyte subclusters of cardiac transcription factors, sarcomere components, and ECM components defining the cardiomyocyte cell-state identities. \u003cstrong\u003eD.\u003c/strong\u003e Scatterplot comparing average expression of cardiac genes in EHT cardiomyocytes and 2D monolayer hiPSC-CMs. Dotted red line represents the y=x line.\u003c/p\u003e","description":"","filename":"Figure2ZWR.png","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/d518e9adb5a6c325a7685e2a.png"},{"id":92134201,"identity":"7f0fca67-192c-4c9f-92aa-904d2a30928e","added_by":"auto","created_at":"2025-09-25 04:06:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":294463,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOpen Chromatin Landscape of Engineered Heart Tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eSchematic overview depicting processing of day 30 EHTs into single cell suspension for flow cytometry to separate GFP+ cells from GFP- cells. EHTs were generated as GFP+ hIPSC-CMs combined with GFP- primary cardiac fibroblasts. \u003cstrong\u003eB\u003c/strong\u003e. Peak counts measured in each of the generated OCR datasets from EHTs, in comparison to hIPSC-CM and ENCODE LV. \u003cstrong\u003eC.\u003c/strong\u003eGenomic context of OCRs found in each data set demonstrating the overall distribution of OCRs is similar across cell or tissue sources. \u003cstrong\u003eD. \u003c/strong\u003eVenn Diagram showing overlap between OCRs found in EHTs vs ENCODE LV, and also showing the overlap between EHT-CM OCRs and 2D hiPSC-CM OCRs. \u003cstrong\u003eE.\u003c/strong\u003eATAC-Seq signal plot showing enhanced OCR detection in EHT CM compared to hIPSC-CMs near \u003cem\u003eTNNC1\u003c/em\u003e. Arrow indicates an example of a newly identified OCR region in EHT-CMs compared to ENCODE LV. \u003cstrong\u003eF. \u003c/strong\u003eATAC-Seq signal plot illustrating intermediate OCRs in EHT-CM relative to immature hiPSC-CMs and adult left ventricle tissue at the \u003cem\u003eMYH6-MYH7\u003c/em\u003e locus demonstrating example EHT-CM peaks encompassing a hybrid state of both the hiPSC-CM and ENCODE LV peaks in the region, indicated by the black arrows. \u003cstrong\u003eG. \u003c/strong\u003eEnrichment scores of transcription factor binding motifs in EHT-CM OCRs. \u003cstrong\u003eH. \u003c/strong\u003eATAC-Seq signal plot demonstrating deconvolution of adult left ventricle signal into contributions of cardiomyocyte and fibroblast open chromatin regions at \u003cem\u003eDDR2\u003c/em\u003e, indicated with black arrows. \u003cstrong\u003eI. \u003c/strong\u003eEnrichment Scores transcription factor binding motifs in EHT-FB OCRs.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/7390851c79795221f5805d21.png"},{"id":92134008,"identity":"5ed32255-0301-4aeb-8591-8b21c99b55e0","added_by":"auto","created_at":"2025-09-25 03:58:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":152187,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect Size Enrichment and Fine-Mapping to Prioritize Variants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Schematic overview of functional fine-mapping procedure using OCRs as genetic context annotations. \u003cstrong\u003eB. \u003c/strong\u003eGlobal effect-size enrichment results from TORUS of a several of cardiac function and disease and non-cardiac control traits using OCR datasets as genome context annotations. \u003cstrong\u003eC. \u003c/strong\u003eScatter plot of expression scores constructed from gene sets involved in essential cardiac functional pathways and cardiac disease states, compared in EHT CMs and 2D monolayer hiPSC-CMs. \u003cstrong\u003eD. \u003c/strong\u003eComparison of gains and losses in best posterior inclusion probability (PIP) for Cardiomyopathy gene-associated variants versus their standard uniform fine-mapping result.\u003c/p\u003e","description":"","filename":"Figure4ZWR.png","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/78cee21989b7cfae40964c63.png"},{"id":92134012,"identity":"657061a4-7070-4710-8623-4a02a513506b","added_by":"auto","created_at":"2025-09-25 03:58:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":101661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional Genomics of Putative Enhancer Variants at HCM GWAS loci\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eThe top track (black) depicts the HCM GWAS locus zoom plot centered over the \u003cem\u003ePRKCA\u003c/em\u003e promoter and the first two introns of \u003cem\u003ePRKCA\u003c/em\u003e. Each track below depicts the prioritized, fine-mapping results for EHT CM, EHT FB, 2D hiPSC-CM, and ENCODE LV OCR inputs. The GWAS variants are each shown as belonging to the credible set for each ATAC-seq dataset (red circles) or deprioritized (grey circles). Variants selected for enhancer assays are shown as bold black circles with corresponding rs numbers. The variants are depicted superimposed onto the ATAC-seq datasets used for prioritization. \u003cstrong\u003eB. \u003c/strong\u003eAllele specific luciferase reporter assays of enhancer constructs expressing two high confidence SNPs identified from the prioritization. The constructs were expressed in hiPSC-CMs and demonstrated allele-specific enhancer activity. \u003cstrong\u003eC. \u003c/strong\u003eHuman LV \u003cem\u003ecis\u003c/em\u003e eQTL data (accessed Aug. 6 2025) demonstrating allele-specific differences in gene expression of \u003cem\u003ePRKCA\u003c/em\u003e in the context of the prioritized genotypes that showed allele-specific enhancer activity.\u003c/p\u003e","description":"","filename":"Figure5ZWR.png","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/d5cdb513aff84751ccb1cc80.png"},{"id":92134202,"identity":"cbf49363-d310-4ad9-8faf-d2c2bdcbd191","added_by":"auto","created_at":"2025-09-25 04:06:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":166253,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of a distal enhancer harboring DCM GWAS signal at chr3p25.1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eThe top track (black) depicts the DCM GWAS locus zoom plot centered over the intergenic sequence between \u003cem\u003eLSM3 \u003c/em\u003eand\u003cem\u003eSLC6A6.\u003c/em\u003e Chromatin conformation (Hi-C) mapping links this region to \u003cem\u003eSLC6A6\u003c/em\u003eand GRIP2. The tracks below depict prioritized, fine-mapping results for EHT CM, EHT FB, 2D hiPSC-CM, and ENCODE LV OCR inputs. The GWAS variants are each shown as belonging to the credible set for each ATAC-seq dataset (red circles) or deprioritized (grey circles). Variants selected for enhancer assays are shown as bold black circles and identified by their rs name. The Variants are depicted superimposed onto the ATAC-seq datasets used for prioritization. To the right is the same presentation of a further zoomed in genomic region specifically over variants selected for haplotype functional analysis. \u003cstrong\u003eB.\u003c/strong\u003e Variant structure of the four distinct haplotypes found in European ancestry individuals with lead SNP (red) and three SNPs tested in synthetic luciferase reporter assays (black). \u003cstrong\u003eC. \u003c/strong\u003eHaplotype-resolved luciferase reporter assays for enhancer activity. Bars and asterisks represent significance in t-test comparisons (P ≤ 0.05). \u003cstrong\u003eD. \u003c/strong\u003eSchematic representation of synthetic haplotype construction for the testing of B-haplotype SNPs. \u003cstrong\u003eE. \u003c/strong\u003eSynthetic haplotype luciferase assays for B-haplotype variants on the A-haplotype background. \u003cstrong\u003eF. \u003c/strong\u003eSchematic depicting CRISPR-Cas9\u003cstrong\u003e \u003c/strong\u003edeletion of the 6kb region containing the credible set of SNPs, performed in hiPSCs. These genome edited cells were then differentiated to hiPSC-CMs from which RNA was collected and sequenced. \u003cstrong\u003eG.\u003c/strong\u003echr3p25.1 enhancer-deleted hIPSC-CMs resulted in reduced expression of both \u003cem\u003eSLC6A6 \u003c/em\u003eand \u003cem\u003eGRIP2\u003c/em\u003e compared to isogenic controls, directly demonstrating the functional role of this enhancer in controlling the expression of multiple genes simultaneously.\u003c/p\u003e","description":"","filename":"Figure6DD.png","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/2fa474369fae337c5907f74b.png"},{"id":92135116,"identity":"ff593960-0bff-4eb1-a36d-9e82c1f3a930","added_by":"auto","created_at":"2025-09-25 04:22:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4279574,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/f22f5601-e570-42e6-a65f-1f186860bbea.pdf"},{"id":92134853,"identity":"15434225-b00e-41fe-8dcb-bc2b6d898fbf","added_by":"auto","created_at":"2025-09-25 04:14:08","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9726921,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Tables\u003c/p\u003e","description":"","filename":"SupplementalTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/8651132e9164afed6228e031.xlsx"},{"id":92134006,"identity":"e8c72220-22a3-459d-a126-817df42a8180","added_by":"auto","created_at":"2025-09-25 03:58:08","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3802368,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Figures\u003c/p\u003e","description":"","filename":"SupplementalFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/6380d44936cb6362842b9711.pdf"},{"id":92134010,"identity":"9d730be4-8bca-428d-b704-370ca73df8c8","added_by":"auto","created_at":"2025-09-25 03:58:08","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":91120,"visible":true,"origin":"","legend":"\u003cp\u003eDetailed Methods\u003c/p\u003e","description":"","filename":"DetailedMethods.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7696371/v1/d2276c22942fdafae41b2b97.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEngineered heart tissues facilitate functional characterization of noncoding variants implicated in Hypertrophic and Dilated Cardiomyopathy\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdvances in human biobank genomics have dramatically increased our ability to identify candidate genetic risk variants for both common and rare diseases [1]. These conditions include cardiomyopathies \u0026ndash;both dilated [2-6]\u0026nbsp;and hypertrophic\u0026nbsp;[2, 7, 8], as well as arrhythmogenic conditions such as Long QT Syndrome\u0026nbsp;[9]\u0026nbsp;and Brugada Syndrome\u0026nbsp;[10, 11]. For example, two recent large-scale genome-wide association studies (GWAS) of dilated cardiomyopathy (DCM) report 80 and 70 risk loci respectively\u0026nbsp;[5, 6], and one hypertrophic cardiomyopathy study (HCM) highlights at least 70 risk loci\u0026nbsp;[8, 12]. These common variants generally map to regions noncoding regions, often distinct from monogenic genes for cardiomyopathy. The noncoding GWAS signals likely impart phenotypic effects through distal gene-enhancer interactions that regulate gene expression. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe dozens of GWAS loci associated with both human HCM and DCM provide a much-expanded genomic context for variation contributing to these diseases. Nevertheless, it is challenging to identifying the regulatory variants that directly affect gene targets versus those that are simply linked to functional regions. The lack of conservation in noncoding regions across species renders assessment nontrivial\u0026nbsp;in traditional model systems like mice. Human-specific myocardial disease models have largely relied on monoculture induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) [13, 14]. These models provide a critical experimental substrate, namely the complete human genome. Nonetheless, hiPSC-CMs are limited in both physiological and transcriptional maturity [15-17]. They also lack the cellular diversity present in developing and adult heart tissue, limiting the study of cell-type interactions and genetic pleiotropy.\u003c/p\u003e\n\u003cp\u003eThe development of three dimensional differentiated organoid cultures [18-20], and engineered tissues [21, 22] has progressively begun to mitigate concerns about maturation level and cell-type diversity to increase model fidelity and increase translational capacity in cardiac research. Here, we generated engineered heart tissues (EHTs) comprised of hiPSC-derived cardiomyocytes and primary human cardiac fibroblasts in a collagen-based scaffold in long term co-culture [23]. EHTs were interrogated using single-cell RNA and ATAC-sequencing, highlighting their enhanced maturity compared to hiPSC-CMs in 2D monolayer cultures. Using comprehensive regulatory maps generated from EHTs, 2D monolayer hiPSC-CMs, and ENCODE adult left ventricle ATAC-Seq [24, 25], we functionally fine-mapped 122 independent genomic loci from 12 GWAS cohorts of DCM and HCM. We identified thousands of prioritized variant candidates positioned to contribute to HCM and DCM, including their potential cell-type specificity. We validated several prioritized regions as enhancers, revealing haplotype-specific differences and links to specific effector genes, including\u0026nbsp;\u003cem\u003eSLC6A6\u0026nbsp;\u003c/em\u003ewhich encodes a taurine receptor and\u003cem\u003e\u0026nbsp;GRIP2\u003c/em\u003e, encoding a protein that interacts with ephrin-B ligands. These data additionally provide a resource of epigenetic and expression data, along with functional fine-mapping results, to identify and test the effects of genetic variation on myocardial function.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cu\u003eDifferentiation of human iPSC to Cardiomyocytes\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eVentricular hiPSC-CMs were generated by standard methods using small molecule modulation of WNT signaling [26, 27] adapted from previously published protocols. Briefly, when hiPSCs reached ~90% confluency, differentiation was initiated using 6mM CHIR99021(Tocris) for 24 hours in cardiomyocyte differentiation medium 3 (CDM3): RPMI 1640 (Thermo Fisher Scientific) supplemented with 2mM L-glutamine (Gibco), 213 mg/mL L-ascorbic acid 2-phosphate (Wako Chem), and 500mg/mL recombinant human albumin (Sigma). Media containing CHIR99201 was exchanged for fresh CDM3 at 24 hours. On day 3 cells were treated with 2mM Wnt-C59 (Tocris) in CDM3. On day 5, c59-containing media was changed for CDM3 and subsequently exchanged every two days until cells matured into beating monolayers. On day 10 of differentiation, beating cardiomyocytes were dissociated to single cells using 200units/mL collagenase IV (Worthington Biochemical Corporation) in Hank\u0026rsquo;s Balanced Salt Solution (Thermo) with 10mM HEPES, 2mM Thiazovivin (STEMCELL Technologies) and 30 mM N-Benzyl-p-Toluenesulfonamide (TCI) for 2 hours at 37\u0026deg;C. The single cell suspension of iPSC-CMs was filtered through a 100mm cell strainer and purified via PSC-Derived Cardiomyocyte Isolation Kit, human (Miltenyi Biotec), which routinely yields \u0026gt;90% purity, as assessed by flow cytometry from Cardiac Troponin T (BD Biosciences).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eGeneration of Engineered Heart Tissues\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eEngineered Heart Tissues were generated according to our previously published protocol [23], derived from the procedure originally outlined in by Tiburcy et al., [21, 22]. Briefly, we purified eGFP expressing hiPSC-CMs using column a column-based purification and combined them with human cardiac fibroblasts (Promocell) in a 9:1 ratio for a total of 0.55x10\u003csup\u003e6\u003c/sup\u003e cells per EHT in custom 48-well plates (Myriamed). Cells were suspended in RPMI containing B-27 supplement (Thermo Fisher Scientific) and combined over ice with 44 \u0026mu;L collagen type I (6.5 mg/mL, Millipore Sigma), 44 \u0026mu;L of 2\u0026times; RPMI (Thermo Fisher Scientific), and 6 \u0026mu;L of 0.1N NaOH for a total of 198 \u0026mu;L per EHT, creating a hydrogel substrate mixture. 180 \u0026mu;L of the mixture was then transferred evenly into each well of the 48-well plate for EHT formation, resulting in a final cell density of 500,000 cells/well. Following the transfer, EHTs were incubated the 48-well plate at 37\u0026deg; C for 1 hour and before supplementing with EHT media containing DMEM low glucose (Millipore Sigma), 10% horse serum (heat inactivated, New Zealand origin, Gibco), 1% Penicillin/Streptomycin (Thermo Fisher Scientific), and 0.1% human insulin (10 mg/mL, Millipore Sigma). For 72 hours following EHT generation, the cells were supplemented with 5 \u0026mu;g/mL recombinant human TGF-\u0026beta;1 (CHO derived, PeproTech). On day 3 after tissue casting, TGF-\u0026beta;1 was removed from culture medium and media was exchanged with 500 \u0026mu;L fresh media per well. EHT media was exchanged every 2 days throughout the course of tissue maturation. EHTs were harvested for downstream applications at day 20 post tissue casting.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eImmunofluorescence Imaging of EHTs and 2D hIPSC-CMs\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eFor iPSC-CM monolayer imaging (2D), 3x10\u003csup\u003e5\u003c/sup\u003e cells were plated in 24‐well plate 12\u0026thinsp;mm on Matrigel‐coated micro cover glass slips (Electron Microscopy Sciences). Cells were washed with 30mM KCl in PBS and fixed with 10% formalin for 10 minutes, and then permeabilized with 0.2% Triton. They were then blocked for one hour with 10% donkey serum-PBS and then incubated overnight at 4\u0026deg;C with MYBPC3 Antibody (E-7, Santa Cruz) diluted 1:400 in 10% Donkey Serum-PBS, followed by a wash with 0.1% Tween-PBS. Next, they were incubated 1 hour at room temperature in 1:400 Anti-mouse Alexa 594 (ThermoFisher) in 10% donkey serum and then washed again with 0.1% Tween-PBS, then PBS. Nuclei were stained with DAPI (1:1000) in PBS, washed again with PBS, and then mounted onto glass slides with Prolong Gold (Thermo) and imaged on a Zeiss Axio Imager M2.\u003c/p\u003e\n\u003cp\u003eEHTs were removed from the plates, washed with 30mM KCl in PBS, and fixed overnight in 10% formalin at 4\u0026deg;C. The EHTs were then washed in PBS and permeabilized with 0.5% Triton in PBS, three times for 30 minutes each time. They were then washed in PBS and equilibrated with 15% sucrose in PBS for 5 minutes, followed by 30% sucrose-PBS for 5 minutes. These equilibrated EHTs were then placed into Tissue-Tek\u0026reg; O.C.T. Compound (Sakura) and flash frozen in LiN2. Frozen EHTS were sectioned at 15\u0026micro;m onto glass slides. These sections were then blocked and stained using the same method as described for the 2D monolayers.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSingle-Cell RNA-Sequencing of EHTs\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTo produce inputs for single cell transcriptomic datasets of EHTs, 2 x 48-well plates of EHTs were generated, representing two independent differentiations of input cardiomyocytes. The tissues were then pooled and incubated them at 37\u0026deg; C for 1-2 hours in a dissociation solution containing 1mg/ml Collagenase II (200 units/mg, Worthington),10 mM HEPES pH 7.4 (Gibco), 2 uM Thiazovivin (STEMCELL Technologies), and 30 uM N-Benzyl-p-Toluenesulfonamide, a contractility inhibitor, (TCI) to obtain a single cell suspension. A single cell RNA-sequencing library targeting 10,000 cells was prepared from this suspension using the 10X Genomics Chromium Platform at the NUSeq Core Facility at Northwestern University, Feinberg School of Medicine. We then repeated this procedure for a second time with two additional EHT differentiations to prepare an independent, replicated experiment. Both libraries were sequenced on the Illumina NovaSeq platform in 50bp paired-end format. Reads were aligned to the GRCh38.p13 transcriptome using the STAR [28] aligner as integrated into the 10X Genomics Cell Ranger count pipeline [29]. Cell x gene count matrices were then filtered on mitochondrial read content and total RNA content, as well as predicted doublet status, using Scrublet [30]. The datasets were then clustered, integrated, and analyzed using the Seurat package [31, 32] for the R programming language.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eBulk RNA-Sequencing of 2D hiPSC-Cardiomyocytes\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eDay 10 differentiated hiPSC cardiomyocytes were released from cell culture plates using trypsin-EDTA (Thermo Fisher Scientific), pelleted and washed in Dulbecco\u0026rsquo;s phosphate-buffered saline solution (DPBS) without calcium or magnesium (Thermo Fisher Scientific) and RNA was extracted using Qiagen RNeasy mini kit (Qiagen). Libraries were prepared using the NEBNext low input RNA library prep kit for Illumina (NEB) with NEBNext Illumina index primers and adapters. Sequencing of the RNA-Seq libraries was performed at the University of Chicago on an Illumina NovaSeq 6000 platform, and reads were aligned to the GRCh38/hg38 human transcriptome reference with NCBI refseq transcript models. Library strand orientation was confirmed using kallisto [33] and count matrices were generated by HTSeq-count [34]. Differential expression analyses were performed in R using the \u003cem\u003elimma\u003c/em\u003e [35], and \u003cem\u003eedgeR\u0026nbsp;\u003c/em\u003e[36]\u0026nbsp;packages.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eATAC-Seq in EHTs and 2D hiPSC-CMs\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eDay 30 EHTs were digested to a single cell suspension as described above and sent for fluorescence-activated cell sorting (FACS), gating on expression of eGFP. eGFP- fibroblasts were separated from the eGFP+ iPSC-CMs, producing CM enriched (CM+) and fibroblast enriched (FB+) cell pools. The sorted cells were washed again with 10mL EHT media and resuspended in 1mL with 2%BSA. The cells were then counted and separated into aliquots of 50,000. Assay for transposase-accessible chromatin sequencing (ATAC-Seq) was performed as described by Grandi, F. C. et al. [37]. Briefly, 50k cells were collected at 500g for 5min at 4\u0026deg;C and gently resuspended in 50\u0026micro;l ice-cold lysis buffer: 10 mM Tris\u0026ndash;HCl pH 7.5, 10 mM NaCl, 3 mM MgCl2, 0.1% NP40 (Sigma), 0.1% Tween-20 (Thermo Fisher Scientific), and 0.01% digitonin (Promega). Lysis proceeded for 3min on ice and was neutralized with 1ml of ice-cold lysis buffer excluding detergents digitonin and NP40. Following neutralization, permeabilized nuclei were pelleted at 4\u0026deg;C and resuspended in 50\u0026micro;l transposition master mix: 25\u0026micro;l (2X) Illumina TD buffer, 16.5\u0026micro;l PBS, 5\u0026micro;l H2O, 0.5\u0026micro;l 1% digitonin, 0.5\u0026micro;l 10% Tween-20, and 2.5\u0026micro;l Illumina TDE1 enzyme (Illumina), and incubated for 30min @ 37\u0026deg;C on a thermomixer. Tagmentation was terminated and DNA collected using Zymo Clean and Concentrator-5 (Zymo) following the manufacturers recommendations. Libraries were barcoded with 5 cycles of preamplification, and the PCR reaction was paused for quantification using NEBNext Library Quant Kit for Illumina (New England Biosciences). Diluted final libraries were mixed and sequenced on an Illumina Nova-Seq 6000 using paired end 50bp reads with a target depth of ~300M reads/sample. Detailed descriptions of read processing, peak calling, and downstream analyses of ATAC-Seq libraries can be found in the Detailed Methods section of the supplement.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEffect Size Estimates and Functionally informed Fine-mapping\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eSummary statistics for fine-mapped traits (\u003cstrong\u003eSupplemental Table 1\u003c/strong\u003e) were lifted over to GRCh38.p13/hg38 in R v.4.1.0 using the \u003cem\u003eliftOver\u003c/em\u003e package (https://doi.org/10.18129/B9.bioc.liftOver). Effect-size enrichment estimates and SNP-level prior weights were obtained in each dataset using TORUS [38], where loci were split into 1600 distinct LD blocks across the human genome for estimation. The prior weights were then utilized in functionally informed fine-mapping in R with the \u003cem\u003esusieR\u0026nbsp;\u003c/em\u003e[39] and \u003cem\u003emapgen\u003c/em\u003e [40] packages to obtain estimates of posterior inclusion probabilities at each locus in both HCM and DCM. The estimates were compared with uniform prior fine-mapping in the same tools, and loci were prioritized for further study based on their apparent localization to distal regulatory elements and the exclusion of high probability coding variants in the credible sets.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eLuciferase Assays\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eGenomic sequences used in luciferase reporter assays for fine-mapping at \u003cem\u003ePRKCA, VTI1A\u0026nbsp;\u003c/em\u003e/ \u003cem\u003eTCF7L2,\u0026nbsp;\u003c/em\u003eand the \u003cem\u003eGATA4\u0026nbsp;\u003c/em\u003elocus were designed as 1000-bp sequences centered on the variant of interest (\u003cstrong\u003eSupplemental Table 2\u003c/strong\u003e), with an additional 5\u0026rsquo; homology sequence (GGCCTAACTGGCCGgtacctgagctcgctagcctcga) and 3\u0026rsquo; homology sequence (atcaagatctggcctcggcggccaaGCTTAGACACTA) for Gibson assembly into the Promega pgL4.23[\u003cem\u003eluc2\u003c/em\u003e/minP] backbone. These sequences were synthesized as linear IDT gBlocks. The backbone was propagated in NEB Stable Competent E. coli, linearized by XhoI and EcoRV (NEB), and assembled on a thermocycler using NEB Gibson Master Mix. Constructs were dialyzed in deionized water using Millipore MF membrane filters and transformed into NEB stable E coli for propagation, isolation, and sequencing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLonger haplotype sequences for luciferase assays of the chr3p25.1 enhancer (\u003cstrong\u003eSupplemental Table 2\u003c/strong\u003e) were synthesized and delivered as complete plasmids by VectorBuilder, and were similarly propagated, isolated, and sequenced before transfection into hiPSC-CMs. For transfection and activity readings, hiPSCs were differentiated to cardiomyocytes using methods described above, isolated at day 10 using column purification, then plated at 30,000 cells per well into clear bottomed, opaque-walled 96-well plates designed for use with Promega plate-readers (Corning costar). Cells were maintained in RPMI 1640 supplemented with B27 and L-Glutamine. After 24 hours, media was exchanged for media containing 2 uM CHIR99201 and was replaced every 48 hours for the duration of the culture. When cells reached 90% confluency, they were co-transfected with experimental constructs and Renilla normalization controls (Promega) into cells. 20 hours-post transfection, cells were washed, and media was replaced with CHIR99201 containing media. \u0026nbsp;At 48-hours post transfection, cells were lysed, and luciferase activity was read using Promega Glowmax Multi-detection system and Promega Dual-Luciferase Reporter Assay System kit.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCRISPR Editing of hiPSCs\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain hiPSC cell lines with one or more copies of the chr3p25.1 enhancer region removed, we made use of the IDT CRISPR Alt-R genome editing system. Briefly, we obtained four custom crRNAs targeting each of the flanks of the enhancer region (\u003cstrong\u003eSupplemental Table\u003c/strong\u003e \u003cstrong\u003e2\u003c/strong\u003e) and complexed them with Alt-R tracrRNAs (IDT) and Alt-R S.p. Cas9 V3 (IDT) to form four distinct editing ribonuclear proteins (RNPs). These RNPs were pooled in equal quantity and nucleofected into approximately 1x10\u003csup\u003e6\u003c/sup\u003e hiPSCs per manufacturers\u0026rsquo; recommendations using the Lonza 4D Nucleofector System and the Amaxa P3 Primary Cell X Kit L (Lonza). After nucleofection, cells were plated in a single well of a 6-well plate with media containing ROCK inhibitor. Nucleofected cells were left to recover for 48 hours then sorted using FACS at one cell/well into Matrigel coated 96-well plates. Cell colonies were then expanded and passaged until enough material for genotyping could be collected. To genotype edited cells, cell scrapes were taken from 24-well plates and DNA was extracted using Lucigen QuickExtract Buffer (Biosearch Technologies). The genotype of the cells was assayed using PCR with custom primers spanning the target deletion breakpoints, as well as internal primers used to distinguish heterozygous cells. Selected cells were further expanded and banked. Their genotypes were confirmed via additional DNA isolations of larger cell pellets using the Promega Wizard Genomic DNA purification kit, PCR, and Sanger sequencing.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cu\u003eGeneration of Engineered Heart Tissues as a Model of Human Myocardium\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAlthough monolayer hiPSC-CM cultures have revolutionized cardiac disease research [13, 14], their cellular immaturity limits their potential as an experimental system. Engineered heart tissue or engineered heart myocardium [18-22] systems have been developed by mixing hiPSC-CMs with fibroblasts and other cell types, and then placing cell mixtures onto bio-scaffolds where they mature under tension. \u0026nbsp;There are multiple formats for tissue generation, varying in bio-scaffold content and form, ranging from simple strips to rings or even bio-printed ventricular shapes [41]. Here, we molecularly characterized the transcriptomic and open chromatin landscapes of human EHTs and compared these data to hiPSC-CMs in 2D cultures and to left ventricle (LV) myocardium.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo generate EHTs, eGFP-expressing human iPSCs were differentiated to cardiomyocytes in monolayer cell culture for 10 days using WNT modulation followed by depletion of stem cells and enrichment for cardiomyocytes [21-23, 42]. Enriched cardiomyocytes were combined 9:1 with fibroblasts isolated from human hearts in a three-dimensional collagen-based scaffold, forming approximately 15mm ring-like EHTs around two posts. The tissues were then matured for an additional 20 days, totaling 30 days from hiPSC-CM induction to mature EHTs (\u003cstrong\u003eFigure\u003c/strong\u003e \u003cstrong\u003e1A\u003c/strong\u003e). Immunofluorescence imaging with an antibody to cMyBP-C in EHTs revealed cardiomyocyte alignment as well as sarcomere alignment parallel to the axis of contraction (\u003cstrong\u003eFigure 1B\u003c/strong\u003e). By comparison, 2D monolayer hiPSC-CMs show sarcomeres radially aligned around central nuclei, with little cell-cell alignment (\u003cstrong\u003eFigure 1C\u003c/strong\u003e). These characteristics agree with previous morphology described in EHTs [42-44], and further suggest that this cellular alignment more closely models \u003cem\u003ein vivo\u003c/em\u003e cardiomyocytes.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSingle-cell gene expression in EHTs\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the cell-type composition and gene expression patterns within EHTs, we dissociated pools of day 30 EHTs, collected from 2 independent differentiations, into single cell suspensions, and generated single-cell RNA seq libraries using the 10x Genomics Chromium\u003csup\u003eTM\u003c/sup\u003e platform. After sequencing, quality control, and filtering, we obtained 17,081 cells. Unbiased clustering of the transcriptomic data revealed 14 communities (\u003cstrong\u003eSupplement 1A-1C\u003c/strong\u003e) comprising six distinct cell-types (\u003cstrong\u003eFigure 1D\u003c/strong\u003e). By cell number, cardiomyocytes (42.4 %) and cardiac fibroblasts (37.9 %) represented most of the tissue. This cardiomyocyte percentage is similar to estimates found in human hearts from the Heart Cell Atlas (HCA) project, where cardiomyocytes constitute 49% of ventricle cells [45]. SMC/pericyte-like, mesothelial progenitors, neural-like, and endothelial\u0026nbsp;cells contribute the remaining 19.7% of the cell population. EHT cardiomyocytes expressed genes for canonical sarcomere components and known cardiac transcription factors (\u003cstrong\u003eFigure 1E, Supplement 1D\u003c/strong\u003e). Furthermore, average expression of the adult ventricular myosin heavy chain, \u003cem\u003eMYH7\u003c/em\u003e, exceeded that of the fetal isoform \u003cem\u003eMYH6\u003c/em\u003e in EHTs. In general, each major cell-type expressed canonical markers such as \u003cem\u003eFN1\u003c/em\u003e in cardiac fibroblasts, \u003cem\u003eRGS5\u003c/em\u003e in pericyte/SMC cells, \u003cem\u003ePECAM1\u003c/em\u003e in endothelial cells, and \u003cem\u003eSOX2\u003c/em\u003e in neural-like cells (\u003cstrong\u003eFigure 1F\u003c/strong\u003e). As a validation of these cell type annotations, we compared our labels with predictions from CellTypist [46, 47] models trained on the human Heart Cell Atlas. We found general agreement in the predictions of the major cell types, adding confidence regarding EHT composition (\u003cstrong\u003eSupplement 1B\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess potential heterogeneity within the EHT-CM population, we subclustered EHT-CMs at higher resolution with particular attention to sarcomere and transcription factor gene expression. This analysis revealed populations spanning the maturation spectrum (\u003cstrong\u003eFigure 2A\u003c/strong\u003e), aligning well with the second principal component of variation in the expression data (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). We noted a continuum of early, still cycling, and late cardiomyocytes, characterized by differential regulation of canonical adult and neonatal sarcomere genes as well as the specific transitions between \u003cem\u003eMYH6\u0026nbsp;\u003c/em\u003eand \u003cem\u003eMYH7\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e(Figure 2C).\u003c/strong\u003e We also noted subsets of cardiomyocytes across maturation states expressing extracellular matrix components (noted as ECM+). We identified a cardiac progenitor population defined by high expression of the transcription factors \u003cem\u003eGATA4\u003c/em\u003e, \u003cem\u003eTBX20\u003c/em\u003e, and \u003cem\u003eMYOCD\u003c/em\u003e (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). To compare cardiac gene expression between input hiPSC-CMs and EHT-CMs, we performed RNA-seq on 2D hiPSC-CMs. Gene expression was moderately and significantly correlated (r: 0.476334. P-value: 0.0025) between the datasets (\u003cstrong\u003eFigure 2D\u003c/strong\u003e). However, several genes characteristic of mature cardiomyocytes were expressed at substantially higher levels in EHTs, including the sarcomere components \u003cem\u003eTPM1\u003c/em\u003e, \u003cem\u003eACTC1\u003c/em\u003e, \u003cem\u003eTTN\u003c/em\u003e, \u003cem\u003eMYL2\u003c/em\u003e, \u003cem\u003eMYL3\u003c/em\u003e, \u003cem\u003eTNNC1\u003c/em\u003e, and \u003cem\u003eMYH7\u003c/em\u003e. Conversely, we observe only one gene relatively upregulated in 2D hiPSC-CMs, \u003cem\u003eMYH6\u003c/em\u003e, the \u003cem\u003eMYH\u003c/em\u003e isoform that characterizes the developing ventricle, highlighting a difference in maturity between the systems.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eOpen chromatin profiling of engineered heart tissues\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eGiven the advanced maturation of gene expression patterns in EHT-CMs relative to 2D hiPSC-CMs, we speculated that the enhancer landscape of myocardial genes may also more closely resemble that of the adult heart. To test this hypothesis, we generated deep ATAC-Seq datasets from day 30 EHTs and 2D hiPSC-CMs. To separate cell populations into cardiomyocyte-enriched (CM) and fibroblast enriched (FB) pools from which to generate libraries, we sorted the input cell populations using FACS, gated for eGFP signal \u003cstrong\u003e(Figure 3A)\u003c/strong\u003e. For an adult myocardial comparison, we also reprocessed sequencing data from eight libraries of four high quality bio samples taken from left ventricle (LV) tissue originally published for the ENCODE project [24, 25] (\u003cstrong\u003eSupplemental Table 1\u003c/strong\u003e). After peak calling and filtering, this analysis produced a set of 160,270 fixed-width open chromatin regions (OCRs) in the reference ENCODE LV dataset and 153,189 OCRs in the 2D hiPSC-CM ATAC-Seq (\u003cstrong\u003eFigure 3B\u003c/strong\u003e). In the EHT-CM and EHT-FB datasets, we found substantially more OCRs: 221,530 and 185,303 regions respectively, for a combined dataset of 406,833 OCRs. This increased number of OCRs likely reflects the model, which includes developmental and adult gene expression patterns, further enhanced by the uniquely deep sequencing applied to the EHTs.\u003c/p\u003e\n\u003cp\u003eThe pattern of genomic context of the identified OCRs was consistent with what is expected from ATAC sequencing [48], where the plurality of regions mapped to introns, followed by intergenic regions, promoters, exons, and finally transcription termination sites (\u003cstrong\u003eFigure 3C\u003c/strong\u003e). Comparing the EHT ATAC-Seq results to ENCODE LV data reprocessed using the same metrics, we detected 206,077 EHT intersecting at least one element in the ENCODE LV set (\u003cstrong\u003eFigure 3D\u003c/strong\u003e). Of the EHT-CM OCRs, we also found 130,571 annotations (58.9%) overlapped at least one region in the 2D hiPSC-CM set; demonstrating broad overlap with both the adult and early states. We illustrate one example proximal to the cardiac troponin C1 gene, \u003cem\u003eTNNC1\u003c/em\u003e, which is absent in ENCODE LV but found in both EHT and 2D hiPSC-CMs (\u003cstrong\u003eFigure 3E, arrow\u003c/strong\u003e). We also observe many sites where the EHT dataset mimics a hybrid state between the early open chromatin structure of 2D hiPSC-CMs and the mature structure of ENCODE LV tissue. This hybrid state is exemplified at the \u003cem\u003eMYH6\u003c/em\u003e-\u003cem\u003eMYH7\u003c/em\u003e locus where 2D hiPSC-CMs display strong open chromatin signal at a proximal promoter element of the fetal ventricular heavy chain \u003cem\u003eMYH6\u0026nbsp;\u003c/em\u003e(\u003cstrong\u003eFigure 3F, arrows\u003c/strong\u003e). In contrast, the adult LV shows open chromatin approximately 1900 bp upstream, with diminished signal at the proximal promoter.The EHT CM OCR dataset captures both elements with comparable signal strength. These findings suggest EHTs can model genomic perturbations relevant to cardiac development and the more mature myocardium. As an additional validation of the cardiac regulatory elements, we analyzed transcription factor binding sites within EHT CM OCRs. We found canonical cardiac transcription factors motifs among the most enriched in the EHT OCRs (\u003cstrong\u003eFigure 3G\u003c/strong\u003e) including those of MEF2C, GATA4, TBX20, NKX2-5\u003cem\u003e,\u0026nbsp;\u003c/em\u003eand TBX5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCM- and FB-enriched ATAC-Seq data also enable deconvolution of the bulk ENCODE LV signal into relative cell type contributions. For instance, at \u003cem\u003eDDR2\u003c/em\u003e, a gene encoding a collagen-binding receptor tyrosine kinase involved in fibrosis, we detect several promoter elements as well as distal intronic OCRs in the fibroblasts rather than the cardiomyocytes (\u003cstrong\u003eFigure 3H, arrows\u003c/strong\u003e). Motif enrichment within fibroblast footprints suggests an active state, likely reflecting the TGFb\u0026nbsp;supplementation used during the tissue consolidation process. These include motifs corresponding to TCF1, ETS1, FLI1, and NR2F2 (\u003cstrong\u003eFigure 3I\u003c/strong\u003e). These fibroblast-specific sites provide an additional opportunity to assess potential fibroblast contributions to cardiac disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunctional Fine-mapping of Cardiomyopathies from EHTs\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe depth of OCR annotations in EHTs enabled fine-mapping of cardiomyopathy GWAS loci [2, 5-8]. Functionally informed fine-mapping is a two-step, empirical Bayes approach to variant prioritization that uses genomic annotations to estimate how strongly GWAS effect sizes are enriched in specific functional regions. These enrichment estimates are then used as prior probabilities and combined with linkage disequilibrium data to pinpoint variants likely to contribute to trait biology at the locus\u0026nbsp;\u003cstrong\u003e(Figure 4A)\u003c/strong\u003e. We generated effect-size enrichment estimates using TORUS\u0026nbsp;[38]\u0026nbsp;for each OCR dataset, and applied this analysis to EHT-CM, EHT-FB, iPSC-CM, and ENCODE LV data. We used summary statistics from a multi-trait GWAS (MTAG) conducted on cohorts with hypertrophic cardiomyopathy\u0026nbsp;[8]\u0026nbsp;and dilated cardiomyopathy\u0026nbsp;[6], left ventricle ejection fraction (LVEF) and left ventricle end-diastolic volume (LVEDVi)\u0026nbsp;[49]. We also selected two traits unrelated to myocardial tissue: body mass index (BMI)\u0026nbsp;[50]\u0026nbsp;and schizophrenia (SCH)\u0026nbsp;[51]\u0026nbsp;to serve as trait controls.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur estimates reveal strong GWAS effect size enrichment in all cardiac OCRs across each of the cardiac traits (\u003cstrong\u003eFigure 4B\u003c/strong\u003e). Both EHT-CM and ENCODE LV OCRs were enriched for variants associated with contractile traits like LVEF and LVEDVi, as well as DCM and HCM, reflecting the contributions of cardiomyocytes and fibroblasts underlying those phenotypes. By comparison, unrelated traits of body mass index and schizophrenia showed no enrichment (\u003cstrong\u003eFigure 4B\u003c/strong\u003e). \u0026nbsp;The strong enrichment in cardiomyopathy associated GWAS effects may reflect increased maturation of gene regulatory circuits in EHTs across entire pathways, relative to 2D hiPSC-CMs. Assaying pathway-level gene expression in EHT-CMs, we note higher expression in EHT-CMs in the sarcomere, as well as in cardiomyopathy and several other disease-associated gene sets, and sets related to cardiac development and contractile function (\u003cstrong\u003eFigure 4C\u003c/strong\u003e). Taken together, these estimates demonstrate that the open chromatin regions in EHTs are globally enriched for SNPs identified in GWAS of cardiac traits at levels comparable to adult left ventricle.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe next used the enrichment estimates to derive SNP-level prior probabilities for statistical fine-mapping of each GWAS and OCR dataset with SuSieR [39, 52]. SuSieR uses summary statistics data from GWAS studies and linkage disequilibrium data (LD) to perform fine-mapping. At each locus, we obtained a group of SNPs that together have a 90% chance of containing a functional variant, termed a “credible set.” Each SNP was then assigned a posterior inclusion probability (PIP), reflecting the probability that the variant is functional. In total, 5817 SNPs were mapped to at least one credible SNP set in HCM (2545 SNPs across 83 loci) and/or DCM (3870 SNPs across 87 loci) (\u003cstrong\u003eSupplemental Tables 3-5\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found that average credible set sizes were reduced using this prioritized fine-mapping compared to uniform fine-mapping of the same loci. EHT-CM fine-mapping especially outperforms the other analyses in reducing large credible sets of 30 or more SNPs (\u003cstrong\u003eSupplement 2A\u003c/strong\u003e). Most fine-mapped SNPs were selected into more than one credible set across each OCR input, indicating a measure of robustness in the strategy (\u003cstrong\u003eSupplement 2B\u003c/strong\u003e). These SNPs included in multiple credible sets tended to show higher PIPs when using OCR prior probabilities (max functional PIP) when compared to fine-mapping with uniform priors (uniform PIP) (\u003cstrong\u003eFigure 4D\u003c/strong\u003e). This finding suggests that SNPs within open chromatin regions are more likely to be prioritized as potentially functional, as opposed to simply being in LD. A substantial proportion of the entire set of 5817 SNPs were found to intersect with at least one OCR dataset (17.3%), with DCM SNPs in OCRs at a slightly higher rate (20.5%) than HCM SNPs (15.7%) (\u003cstrong\u003eSupplement 2C\u003c/strong\u003e). Most of this overlap could be reproduced in EHT datasets alone (Combined: 13.6%, HCM: 12.4%, DCM 16.2%). These fine-mapping results allowed us to identify individual variants of interest which are likely to be functionally relevant for cardiomyopathy expression, improving the efficiency of downstream variant-to-function analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eInterrogation of Noncoding Variants in HCM and DCM\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWhen fine mapping the HCM and DCM GWAS data, we found that most credible set SNPs mapped within noncoding regions of the genome, including those SNPs identified as having the highest probability of being functional. The annotation indicates that these SNPs are likely to modify gene expression via \u003cem\u003ecis\u003c/em\u003e-regulatory mechanisms, as has been suggested for HCM and DCM associated GWAS signals [53]. To further prioritize loci potentially acting via cis-regulatory mechanisms, we intersected many these credible sets with chromatin conformation capture data which we previously generated in hiPSC-CMs [27].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;We identified four HCM GWAS variants at chr17q24.2 that were strongly prioritized by fine-mapping. These SNPs, rs17633401, rs67700546, rs12601850, and rs9892651, were in a locus spanning the \u003cem\u003ePRKCA\u003c/em\u003e gene, which encodes protein kinase C alpha (\u003cstrong\u003eFigure 5A \u0026amp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplement 3A\u003c/strong\u003e). Both rs67700546 and rs17633401 were in OCRs shared by EHT-CMs and EHT-FBs, while rs12601850 intersects a fibroblast specific element, and rs9892651 did not intersect an OCR in any dataset (\u003cstrong\u003eFigure 5A\u003c/strong\u003e). In enhancer assays in hiPSC-CM, the regions displayed activity, with rs12601850 and rs9892651 conferring allele-specific expression effects (\u003cstrong\u003eFigure 5B)\u003c/strong\u003e. \u0026nbsp;These variants were also identified in GTEx [54] as eQTLs for expression of \u003cem\u003ePRKCA\u003c/em\u003e in left ventricle in the expected direction, adding external support to the presence of one or more functional variants at the locus(\u003cstrong\u003eFigure 5C\u003c/strong\u003e). These results illustrate that the genetic architecture of a given GWAS-associated locus may involve more than one functionally relevant variant affecting different enhancers [55, 56], potentially in different cell types.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe fine-mapped an additional HCM signal within an intron of \u003cem\u003eVTI1A\u003c/em\u003e, a gene implicated in vesicle transport on chr10 at 10q25.2 (\u003cstrong\u003eSupplement 4A\u003c/strong\u003e). The credible set for this signal spans approximately 38 kilobases (kb), with both ends forming distal chromatin interactions with the\u003cem\u003eTCF7L2\u003c/em\u003e promoter approximately 245 kb away. Interestingly, the EHT-CM and ENCODE LV fine-mapping prioritized two different variants; rs7096151 (EHT CM PIP = 0.878) and rs10885378 (ENCODE LV PIP = 0.542), which may be due to the structure of the underlying open chromatin data. rs7096151 lies in an OCR found in EHT-CM and 2D hiPSC-CMs. Conversely, rs10885378 is at the edge of an ENCODE LV OCR (\u003cstrong\u003eSupplement 4A\u003c/strong\u003e). To test whether either variant was located in cardiac enhancers, we expressed 1000-bp sequences centered on each SNP in hiPSC-CMs to assay luciferase reporter activity. rs10885378-T showed enhancer activity, while rs10885378-C was not significantly different from gene desert or rs10885378-T, indicating modest genotype-specificity (\u003cstrong\u003eSupplement 4B\u003c/strong\u003e). Similarly, rs70961651-A trended towards higher enhancer activity than rs70961651-G, but was not significantly different (P=0.09) (\u003cstrong\u003eSupplement 4B\u003c/strong\u003e). The transcriptional regulator TCF7L2 is differentially active in failed hearts [57], thus, it is possible that this SNP might demonstrate stronger effects under conditions of heart failure, or that both regions may contribute and act synergistically.\u003c/p\u003e\n\u003cp\u003eWe also dissected a credible set associated with DCM locus at chr8p23.1. The credible set at this locus spans 30 kb and forms long-range chromatin interactions over 280 kb in hiPSC-CM with the developmental cardiac transcription factor gene, \u003cem\u003eGATA4\u003c/em\u003e (\u003cstrong\u003eSupplement 5A\u003c/strong\u003e). A single variant, rs13260325, strongly prioritized in EHT-CM fine-mapping (PIP = 0.46), ENCODE LV (PIP = 0.577) and 2D-hiPSC-CM (PIP = 0.436), overlapped an OCR shared in all datasets (\u003cstrong\u003eSupplement 5A\u003c/strong\u003e). When placed into a luciferase reporter and transfected into hiPSC-CMs, rs13260325 acts as an enhancer of luciferase gene expression (\u003cstrong\u003eSupplement 5B\u003c/strong\u003e). These data suggest that noncoding variants, albeit at a distance from \u003cem\u003eGATA4\u003c/em\u003e, may impart their effects on through \u003cem\u003ecis\u003c/em\u003e-regulation of \u003cem\u003eGATA4\u003c/em\u003e expression in addition to the established roles of rare coding variants in \u003cem\u003eGATA4\u003c/em\u003e in DCM [58].\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCharacterization of the 3p25.1 HCM Locus\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe identified a DCM-associated region at chr3p25.1 harboring a GWAS signal concentrated in an intergenic region near the \u003cem\u003eLSM3\u0026nbsp;\u003c/em\u003egene previously\u0026nbsp;highlighted in Garnier et al, 2021 [3]. Fine-mapping of the locus prioritized a credible set spanning 6 kb overlapping a dense set of OCRs shared in multiple cardiac data sets (\u003cstrong\u003eFigure 6A\u003c/strong\u003e). This cluster of OCRs forms long-range chromatin interactions with \u003cem\u003eSLC6A6, and GRIP2.\u0026nbsp;\u003c/em\u003eEach fine-mapping approach strongly prioritized SNP rs6807275 (EHT-CM PIP = 0.886). Variants in this DCM-chr3p25.1 credible set are arranged into four predominant haplotypes in European-associated ancestry populations; designated haplotypes ‘A-D’. Haplotypes ‘A’ and ‘B’ occur most frequently (Allele frequencies: A = 0.455; B = 0.240) \u003cstrong\u003e(Figure 6B)\u003c/strong\u003e. Both haplotypes carry the prioritized SNP rs6807275, but differ in three other linked variants (rs6786141, rs7650762, and rs12634565) common to the ‘C-D’ group sequences. Luciferase reporter assays confirmed enhancer activity of each of the four haplotypes expression with levels significantly higher than our negative control non-enhancer sequence (A; P = 9.19x10\u003csup\u003e-8\u003c/sup\u003e, B; P = 6.80x10\u003csup\u003e-10\u003c/sup\u003e, C; P = 1.38x10\u003csup\u003e-6\u003c/sup\u003e, D; P = 6.96x10\u003csup\u003e-6\u003c/sup\u003e) (\u003cstrong\u003eFigure 6C\u003c/strong\u003e). Furthermore, we observed that the ‘A’ haplotype – which is associated increased DCM risk, showed the lowest average enhancer activity, and differed significantly from the ‘B’ haplotype (P=6.72x10\u003csup\u003e-3\u003c/sup\u003e). We hypothesized that one or more of the SNPs differing between ‘A’ and ‘B’ conferred \u003cem\u003ecis\u003c/em\u003e-compensatory, stabilizing enhancer function to mitigate the effect of rs6807275. To test this hypothesis, we created synthetic haplotypes on the ‘A’ background, introducing the single substitutions of each ‘B’ specific variant and similarly tested these sequences in hiPSC-CMs (\u003cstrong\u003eFigure 6D\u003c/strong\u003e). Two of the tested B-Haplotype SNPs, rs6786141 (P =2.29x10\u003csup\u003e-2\u003c/sup\u003e) and rs7650762 (P =2.25x10\u003csup\u003e-2\u003c/sup\u003e) restored enhancer activity, consistent with a compensatory effect (\u003cstrong\u003eFigure 6E\u003c/strong\u003e). Notably, both SNPs are also shared on the two minor ‘C-D’ group haplotypes. Reporter constructs for these haplotypes exhibited intermediate activity relative to A and B, suggesting that regulatory output at this locus reflects combinatorial interactions between at the three variants, highlighting a complex cis-regulatory architecture centered on rs6807275.\u003c/p\u003e\n\u003cp\u003eFinally, we deleted a 6 kb region containing the enhancer haplotypes in hiPSCs using CRISPR-Cas9 genome editing to test its regulatory influence on gene expression, generating heterozygous and homozygous knockout lines. We then differentiated these hiPSCs to CMs and assessed the transcriptome by RNA-seq \u003cstrong\u003e(Figure 6F)\u003c/strong\u003e. In enhancer deleted hiPSC-CMs, the expression of both \u003cem\u003eSLC6A6\u0026nbsp;\u003c/em\u003e(P=9.27x10\u003csup\u003e-4\u003c/sup\u003e)and\u003cem\u003e\u0026nbsp;GRIP2\u003c/em\u003e (P=1.77x10\u003csup\u003e-2\u003c/sup\u003e) was significantly reduced (\u003cstrong\u003eFigure 6G-H\u003c/strong\u003e). Despite being highly expressed in the heart, \u003cem\u003eGRIP2\u003c/em\u003e has not been well studied for its cardiac role. \u0026nbsp;There is evidence for role of the taurine transporter \u003cem\u003eSLC6A6\u0026nbsp;\u003c/em\u003ein cardiomyopathy [2, 3, 59], and our data indicates this genomic region regulates both genes and the possibility that both genes contribute to trait liability at the locus. Together, these results identify the GWAS signal at chr3p25.1 as localizing to a distal enhancer, positioned as a modulator of \u003cem\u003eSLC6A6\u003c/em\u003e and \u003cem\u003eGRIP2\u003c/em\u003e expression. These data indicate multiple variants within a single region can induce changes in expression of more than one target gene and may have broader impacts outside of individual-trait biology.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe genetic understanding of both hypertrophic and dilated cardiomyopathy is rapidly expanding to include significant contributions of common, noncoding variation in the genome. In the last five years, the number of common-variant associated loci have quadrupled from approximately 30 to more than 120 across the two conditions. Consequently, the development of comprehensive functional annotations in cardiac cells coupled with functional genomic analysis carried out in human models of myocardium are increasingly important to bridge the gap between genetic inference and cell biological mechanisms.\u003c/p\u003e\u003cp\u003eIn this work, we used human EHTs to provide a model of human myocardium that is cellularly diverse, and more morphologically and transcriptionally mature than 2D hiPSC-CMs. Because EHTs are derived from human IPSCs, they carry the human genome and are well suited to examine noncoding variation that may not be well conserved across species, yet relevant to the expression of human cardiac traits. Although not fully mature, the intermediate maturation state of EHTs yields an experimental system with broad flexibility as a platform for human cardiac genetics since it can be used to study both developmental and adult-onset conditions. This is illustrated in the functional fine-mapping of GWAS traits, where those traits can have early and late effects mediated by developmentally important genes, genes responsible for long-term maintenance of function, or environmentally responsive genes. We showed that the open chromatin regions from EHT datasets could be integrated into statistical fine-mapping procedures to help concentrate evidence on smaller credible set of genomic variants and narrow downstream priorities for further study. At the same time, EHTs can provide insight into which cell-type components may be more likely to contribute effects on a single-variant basis.\u003c/p\u003e\u003cp\u003eThrough this work, we generated unique datasets that add to existing data derived from human hearts. Combined with statistical fine-mapping tools, this integrated information provides a roadmap for examining genetic variation in noncoding regions that may not have comparable regions in model systems like mice. We found that many GWAS variants located to active cardiac enhancers and that those variants can have allele-specific effects. This data provides experimental evidence for the role of noncoding \u003cem\u003ecis\u003c/em\u003e-regulatory contributions to genetic cardiomyopathy liability. Moreover, this risk is not restricted to genetically mediated cardiomyopathies as these nocoding regions may influence cardiac function in a variety of heart failure etiologies.\u003c/p\u003e\u003cp\u003eTesting a subset of variants identified using this approach, we demonstrate the functional consequence of specific regulatory elements, supporting their contribution to modifying gene expression. Illustrating the power of a combination of refined genomic annotations in EHTs with statistical methods for fine-mapping loci associated with cardiomyopathies, we demonstrate a specific distal enhancer-gene interaction at chr3p25.1 between an upstream regulatory element localized to a DCM GWAS signal. Although this region falls closest to \u003cem\u003eLSM3\u003c/em\u003e, the 3D chromatin mapping data link this region to a region containing the \u003cem\u003eSLC6A6\u003c/em\u003e and \u003cem\u003eGRIP2\u003c/em\u003e genes, both of which are expressed in human myocardium. \u003cem\u003eSLC6A6\u003c/em\u003e encodes the taurine receptor, and loss of function variants in SLC6A6 cause a potentially treatable form of cardiomyopathy associated with low taurine content [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Taurine supplementation has been studied as a in people with heart failure and cardiomyopathy [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], however, these studies were not conducted with consideration of underlying genotypes that influence expression of its receptor. Notably, this region was previously identified in GWAS for DCM [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and it was suggested this region likely regulated \u003cem\u003eSLC6AC\u003c/em\u003e. However, we demonstrated that deleting this region from chr3p25.1 markedly reduced both \u003cem\u003eSLC6AC\u003c/em\u003e and \u003cem\u003eGRIP2\u003c/em\u003e, indicating this region regulates more than one gene. GRIP2 is enriched in cardiac expression and encodes an ephrin B1 interacting protein [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and ephrin B1 is implicated in heart development and function [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. This example demonstrates how the fine-mapping and refined analysis, combined with functional genomic annotations in EHTs, can serve as a critical ancillary tool to link genetic variation associated with cardiomyopathies with genes and their function.\u003c/p\u003e\u003cp\u003eAdditionally, the EHT OCR dataset generates a major addition to previously available data. Using these datasets, we can detect previously unannotated open chromatin signals, capture temporal or transient regulatory dynamics in cardiac biology, open avenues for study in fibroblast specific regulation of cardiac traits, and leverage powerful functional fine-mapping paradigms to increase the efficiency of discover from genome-wide association studies. Finally, this work provides a wealth of new, publicly available resources broadly applicable to cardiac disease genetics. Our comprehensive OCR datasets add substantially to known open chromatin annotations of hiPSC-differentiated cardiomyocytes and primary cardiac fibroblasts. Additionally, our fine-mapping results serve as an anchor point for a wide variety of downstream assays and avenues of future study.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eATAC-seq\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Assay for Transposase-Accessible Chromatin Sequencing\u003c/p\u003e\n\u003cp\u003eCM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cardiomyocyte\u003c/p\u003e\n\u003cp\u003eDCM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Dilated Cardiomyopathy\u003c/p\u003e\n\u003cp\u003eECM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Extracellular Matrix\u003c/p\u003e\n\u003cp\u003eEHT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Engineered Heart Tissue\u003c/p\u003e\n\u003cp\u003eFACS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fluorescence-Activated Cell Sorting\u003c/p\u003e\n\u003cp\u003eFB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fibroblast\u003c/p\u003e\n\u003cp\u003eHCM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hypertrophic Cardiomyopathy\u003c/p\u003e\n\u003cp\u003ehiPSC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Human Induced Pluripotent Stem Cells\u003c/p\u003e\n\u003cp\u003ehiPSC-CM\u0026nbsp; \u0026nbsp; \u0026nbsp;Human Induced Pluripotent Stem Cell Derived Cardiomyocyte\u003c/p\u003e\n\u003cp\u003eGWAS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Genome Wide Association Study\u003c/p\u003e\n\u003cp\u003eOCR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Open Chromatin Region\u003c/p\u003e\n\u003cp\u003eSNP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Single Nucleotide Polymorphism\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments:\u003c/p\u003e\n\u003cp\u003eThis research was supported in part through contributions provided by the Genomics Compute Cluster which is jointly supported by the Feinberg School of Medicine, the Center for Genetic Medicine, and Feinberg's Department of Biochemistry and Molecular Genetics, the Office of the Provost, the Office for Research, and Northwestern Information Technology. The Genomics Compute Cluster is part of Quest, Northwestern University's high performance computing facility, with the purpose to advance research in genomics. We thank the Robert H. Lurie Comprehensive Cancer Center of Northwestern University in Chicago, IL, for the use of the Flow Cytometry Core Facility, which provided cell sorting services.\u003c/p\u003e\n\u003cp\u003eSources of Funding:\u003c/p\u003e\n\u003cp\u003eThis work is supported by\u0026nbsp;NIH HL128075 (EMM), NIH HL168239 (TOM),\u0026nbsp;HL131914 (EMM),\u0026nbsp;CA060553 (Flow cytometry core); American Heart Association Strategically Focused Research Network on Arrhythmia and Sudden Cardiac Death; Leducq TransAtlantic Foundation. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDisclosures:\u003c/p\u003e\n\u003cp\u003eEMM consults for PepGen, Tenaya Therapeutics, Novartis, and is a founder of Ikaika Therapeutics and Kardigan. \u0026nbsp; These activities are unrelated to the content of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang, Q., et al., \u003cem\u003eRare variant contribution to human disease in 281,104 UK Biobank exomes.\u003c/em\u003e Nature, 2021. \u003cstrong\u003e597\u003c/strong\u003e(7877): p. 527-532.\u003c/li\u003e\n\u003cli\u003eTadros, R., et al., \u003cem\u003eShared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect.\u003c/em\u003e Nat Genet, 2021. \u003cstrong\u003e53\u003c/strong\u003e(2): p. 128-134.\u003c/li\u003e\n\u003cli\u003eGarnier, S., et al., \u003cem\u003eGenome-wide association analysis in dilated cardiomyopathy reveals two new players in systolic heart failure on chromosomes 3p25.1 and 22q11.23.\u003c/em\u003e Eur Heart J, 2021. \u003cstrong\u003e42\u003c/strong\u003e(20): p. 2000-2011.\u003c/li\u003e\n\u003cli\u003eVillard, E., et al., \u003cem\u003eA genome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy.\u003c/em\u003e Eur Heart J, 2011. \u003cstrong\u003e32\u003c/strong\u003e(9): p. 1065-76.\u003c/li\u003e\n\u003cli\u003eZheng, S.L., et al., \u003cem\u003eGenome-wide association analysis provides insights into the molecular etiology of dilated cardiomyopathy.\u003c/em\u003e Nat Genet, 2024. \u003cstrong\u003e56\u003c/strong\u003e(12): p. 2646-2658.\u003c/li\u003e\n\u003cli\u003eJurgens, S.J., et al., \u003cem\u003eGenome-wide association study reveals mechanisms underlying dilated cardiomyopathy and myocardial resilience.\u003c/em\u003e Nat Genet, 2024. \u003cstrong\u003e56\u003c/strong\u003e(12): p. 2636-2645.\u003c/li\u003e\n\u003cli\u003eHarper, A.R., et al., \u003cem\u003eCommon genetic variants and modifiable risk factors underpin hypertrophic cardiomyopathy susceptibility and expressivity.\u003c/em\u003e Nat Genet, 2021. \u003cstrong\u003e53\u003c/strong\u003e(2): p. 135-142.\u003c/li\u003e\n\u003cli\u003eTadros, R., et al., \u003cem\u003eLarge-scale genome-wide association analyses identify novel genetic loci and mechanisms in hypertrophic cardiomyopathy.\u003c/em\u003e Nat Genet, 2025.\u003c/li\u003e\n\u003cli\u003eLahrouchi, N., et al., \u003cem\u003eTransethnic Genome-Wide Association Study Provides Insights in the Genetic Architecture and Heritability of Long QT Syndrome.\u003c/em\u003e Circulation, 2020. \u003cstrong\u003e142\u003c/strong\u003e(4): p. 324-338.\u003c/li\u003e\n\u003cli\u003eBarc, J., et al., \u003cem\u003eGenome-wide association analyses identify new Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility.\u003c/em\u003e Nat Genet, 2022. \u003cstrong\u003e54\u003c/strong\u003e(3): p. 232-239.\u003c/li\u003e\n\u003cli\u003eIshikawa, T., et al., \u003cem\u003eBrugada syndrome in Japan and Europe: a genome-wide association study reveals shared genetic architecture and new risk loci.\u003c/em\u003e Eur Heart J, 2024. \u003cstrong\u003e45\u003c/strong\u003e(26): p. 2320-2332.\u003c/li\u003e\n\u003cli\u003eMiyazawa, K., et al., \u003cem\u003eCross-ancestry genome-wide analysis of atrial fibrillation unveils disease biology and enables cardioembolic risk prediction.\u003c/em\u003e Nat Genet, 2023. \u003cstrong\u003e55\u003c/strong\u003e(2): p. 187-197.\u003c/li\u003e\n\u003cli\u003eGai, H., et al., \u003cem\u003eGeneration and characterization of functional cardiomyocytes using induced pluripotent stem cells derived from human fibroblasts.\u003c/em\u003e Cell Biol Int, 2009. \u003cstrong\u003e33\u003c/strong\u003e(11): p. 1184-93.\u003c/li\u003e\n\u003cli\u003eZhang, J., et al., \u003cem\u003eFunctional cardiomyocytes derived from human induced pluripotent stem cells.\u003c/em\u003e Circ Res, 2009. \u003cstrong\u003e104\u003c/strong\u003e(4): p. e30-41.\u003c/li\u003e\n\u003cli\u003eTu, C., B.S. 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Recent biobank-scale genome-wide association studies (GWAS) suggest significant polygenic contributions to cardiovascular diseases, including cardiomyopathy. Most GWAS loci map to noncoding regions, which are poorly conserved across species, requiring a human genome context for experimental validation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe created engineered heart tissues (EHTs) from human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes and primary cardiac fibroblasts. We assayed single-cell gene expression and chromatin accessibility to generate comprehensive genome-wide regulatory maps. Open chromatin regions (OCRs) were integrated with chromatin contact information and used to functionally fine-map single nucleotide polymorphisms (SNPs) in cardiomyopathy GWAS. SNPs and their associated regulatory regions were assessed using reporter assays, genome editing, and expression profiling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSingle-cell RNA-seq of EHTs confirmed populations recapitulating major cell types found in hearts, with advanced cardiomyocyte maturation compared to monolayer hiPSC-cardiomyocytes. More than 400,000 OCRs were resolved to cell type and assayed for canonical transcription factor footprints. Functional fine-mapping of GWAS loci prioritized 5817 variants, and reporter assays on select variants validated allele-specific enhancer activity. We identified a locus harboring significant GWAS signals from both dilated cardiomyopathy and left ventricle ejection fraction in an intergenic region at chr3p25.1. Several of these variants lie in OCRs participating in long range chromatin interactions with \u003cem\u003eSLC6A6\u003c/em\u003e and \u003cem\u003eGRIP2\u003c/em\u003e. Haplotype-resolved and synthetic reporter assays confirmed enhancer activity and narrowed candidate SNPs. CRISPR-deletion of this region reduced expression of both \u003cem\u003eSLC6A6\u003c/em\u003e and \u003cem\u003eGRIP2\u003c/em\u003e, indicating the enhancer regulates the expression of more than one gene.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eEHTs derived from hiPSCs are an experimentally tractable platform for testing the function of noncoding variants as modifiers of cardiomyopathy. Variants fine-mapped from cardiomyopathies using EHT regulatory maps have functional consequences and provide a set of prioritized sites to advance the study of polygenic heart failure liability.\u003c/p\u003e","manuscriptTitle":"Engineered heart tissues facilitate functional characterization of noncoding variants implicated in Hypertrophic and Dilated Cardiomyopathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 03:58:03","doi":"10.21203/rs.3.rs-7696371/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"582fed06-5412-425c-891a-1722af0e8ffe","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55215502,"name":"Epigenetics \u0026 Genomics"},{"id":55215503,"name":"Medical Genetics"},{"id":55215504,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2025-09-25T03:58:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 03:58:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7696371","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7696371","identity":"rs-7696371","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}