AbstractCongenital heart diseases (CHD) are a class of birth defects affecting ∼1% of live births. These conditions are hallmarked by extreme genetic heterogeneity, and therefore, despite a strong genetic component, only a very handful of at-risk loci in CHD have been identified. We herein introduced systems analyses to uncover the hidden organization on biological networks of genomic mutations in CHD, and leveraged network analysis techniques to integrate the human interactome, large-scale patient exomes, the fetal heart spatial transcriptomes, and single-cell transcriptomes of clinical samples. We identified a highly connected network in CHD where most of the member proteins had previously uncharacterized functions in regulating fetal heart development. While genes on the network displayed strong enrichment for heart-specific functions, a sub-group, active specifically at early developmental stages, also regulates fetal brain development, thereby providing mechanistic insights into the clinical comorbidities between CHD and neurodevelopmental conditions. At a small scale, we experimentally verified previously uncharacterized cardiac functions of several novel proteins employing cellular assays and gene editing techniques. At a global scale, our study revealed developmental dynamics of the identified CHD network and observed the strongest enrichment for pathogenic mutations in the network specific to hypoplastic left heart syndrome (HLHS). Our single-cell transcriptome analysis further identified pervasive dysregulation of the network in cardiac endothelial cells and the conduction system in the HLHS left ventricle. Taken together, our systems analyses identified novel factors in CHD, revealed key molecular mechanisms in HLHS, and provides a generalizable framework readily applicable to studying many other complex diseases.

Despite a strong genetic component, only a few genes have been identified in congenital heart diseases (CHDs). We introduced systems analyses to uncover the hidden organization on biological networks of mutations in CHDs and leveraged network analysis to integrate the protein interactome, patient exomes, and single-cell transcriptomes of the developing heart. We identified a CHD network regulating heart development and observed that a sub-network also regulates fetal brain development, thereby providing mechanistic insights into the clinical comorbidities between CHDs and neurodevelopmental conditions. At a small scale, we experimentally verified uncharacterized cardiac functions of several proteins. At a global scale, our study revealed developmental dynamics of the network and observed its association with the hypoplastic left heart syndrome (HLHS), which was further supported by the dysregulation of the network in HLHS endothelial cells. Overall, our work identified previously uncharacterized CHD factors and provided a generalizable framework applicable to studying many other complex diseases. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Anophthalmia and microphthalmia (A/M) are rare birth defects affecting up to 2 per 10,000 live births. These conditions are manifested by the absence of an eye or reduced eye volumes within the orbit leading to vision loss. Although clinical case series suggest a strong genetic component in A/M, few systematic investigations have been conducted on potential genetic contributions owing to low population prevalence. To overcome this challenge, we utilized DNA samples and data collected as part of the National Birth Defects Prevention Study (NBDPS). The NBDPS employed multi-center ascertainment of infants affected by A/M. We performed exome sequencing on 67 family trios and identified numerous genes affected by rare deleterious nonsense and missense variants in this cohort, including de novo variants. We identified 9 nonsense changes and 86 missense variants that are absent from the reference human population (Genome Aggregation Database), and we suggest that these are high priority candidate genes for A/M. We also performed literature curation, single cell transcriptome comparisons, and molecular pathway analysis on the candidate genes and performed protein structure modeling to determine the potential pathogenic variant consequences on PAX6 in this disease.