Abstract Body: Background: Congenital heart disease (CHD) affects ~1% of live births, yet ~60% lack a defined genetic etiology. While regulatory non-coding variants (NCVs) are linked to CHD risk, few are functionally characterized. We previously identified nuclear receptors ERRα/γ as key regulators of cardiomyocyte maturation.
Hypothesis: Rare de novo NCVs that disrupt ERRα/γ binding contribute to CHD pathogenesis.
Methods: A catalog of ERRα/γ binding sites in hiPSC-derived cardiomyocytes (hiPSC-CMs) were used to interrogate whole-genome sequencing from CHD probands (TOPMed). Rare de novo NCVs (MAF <0.5%) within ERR sites were prioritized, annotated to cardiac genes, and functionally assessed via reporter assays and CRISPR–Cas9 editing in hiPSC-CMs.
Results: We identified rare de novo NCVs within ERRα/γ binding sites in probands with CHD, prioritizing 10 variants near genes involved in cardiac maturation and development. Reporter assays showed that a subset significantly disrupted ERR-dependent transcription, indicating functional impairment. We focused on an NCV near TFRC, encoding transferrin receptor 1, a key mediator of iron uptake during cardiac development. TFRC exhibits dynamic, stage-specific expression in the developing mouse heart and during hiPSC-CM differentiation, consistent with a developmental role. Notably, maternal iron deficiency is associated with CHD in offspring. CRISPR–Cas9 introduction of the TFRC NCV into hiPSC-CMs reduced TFRC mRNA and protein levels. This was accompanied by decreased ferritin levels, reflecting impaired intracellular iron storage and disrupted iron homeostasis. These findings demonstrate that this non-coding variant alters ERR-regulated enhancer activity, producing a molecular phenotype consistent with disrupted TFRC regulation.
Conclusions: Our findings identify NCVs within ERR binding sites as novel candidate drivers of CHD. One of the variants impacts the ERR target gene TFRC which aligns with the known association between iron deficiency and CHD. This integrative strategy, combining population-scale genomics with functional validation, establishes a framework for identifying pathogenic NCVs to improve genetic diagnosis and risk stratification in CHD.