CHDbase: A comprehensive knowledgebase for congenital heart disease-related genes and clinical manifestations

Machine Learning in Identifying Marker Genes for Congenital Heart Diseases of Different Cardiac Cell Types

Congenital heart disease (CHD) represents a spectrum of inborn heart defects influenced by genetic and environmental factors. This study advances the field by analyzing gene expression profiles in 21,034 cardiac fibroblasts, 73,296 cardiomyocytes, and 35,673 endothelial cells, utilizing single-cell level analysis and machine learning techniques. Six CHD conditions: dilated cardiomyopathy (DCM), donor hearts (used as healthy controls), hypertrophic cardiomyopathy (HCM), heart failure with hypoplastic left heart syndrome (HF_HLHS), Neonatal Hypoplastic Left Heart Syndrome (Neo_HLHS), and Tetralogy of Fallot (TOF), were investigated for each cardiac cell type. Each cell sample was represented by 29,266 gene features. These features were first analyzed by six feature-ranking algorithms, resulting in several feature lists. Then, these lists were fed into incremental feature selection, containing two classification algorithms, to extract essential gene features and classification rules and build efficient classifiers. The identified essential genes can be potential CHD markers in different cardiac cell types. For instance, the LASSO identified key genes specific to various heart cell types in CHD subtypes. FOXO3 was found to be up-regulated in cardiac fibroblasts for both Dilated and hypertrophic cardiomyopathy. In cardiomyocytes, distinct genes such as TMTC1, ART3, ARHGAP24, SHROOM3, and XIST were linked to dilated cardiomyopathy, Neo-Hypoplastic Left Heart Syndrome, hypertrophic cardiomyopathy, HF-Hypoplastic Left Heart Syndrome, and Tetralogy of Fallot, respectively. Endothelial cell analysis further revealed COL25A1, NFIB, and KLF7 as significant genes for dilated cardiomyopathy, hypertrophic cardiomyopathy, and Tetralogy of Fallot. LightGBM, Catboost, MCFS, RF, and XGBoost further delineated key genes for specific CHD subtypes, demonstrating the efficacy of machine learning in identifying CHD-specific genes. Additionally, this study developed quantitative rules for representing the gene expression patterns related to CHDs. This research underscores the potential of machine learning in unraveling the molecular complexities of CHD and establishes a foundation for future mechanism-based studies.

ENU-based dominant genetic screen identifies contractile and neuronal gene mutations in congenital heart disease

BACKGROUND

Congenital heart disease (CHD) is the most prevalent congenital anomaly, but its underlying causes are still not fully understood. It is believed that multiple rare genetic mutations may contribute to the development of CHD.

METHODS

In this study, we aimed to identify novel genetic risk factors for CHD using an ENU-based dominant genetic screen in mice. We analyzed fetuses with malformed hearts and compared them to control littermates by whole exome or whole genome sequencing (WES/WGS). The differences in mutation rates between observed and expected values were tested using the Poisson and Binomial distribution. Additionally, we compared WES data from human CHD probands obtained from the Pediatric Cardiac Genomics Consortium with control subjects from the 1000 Genomes Project using Fisher's exact test to evaluate the burden of rare inherited damaging mutations in patients.

RESULTS

By screening 10,285 fetuses, we identified 1109 cases with various heart defects, with ventricular septal defects and bicuspid aortic valves being the most common types. WES/WGS analysis of 598 cases and 532 control littermates revealed a higher number of ENU-induced damaging mutations in cases compared to controls. GO term and KEGG pathway enrichment analysis showed that pathways related to cardiac contraction and neuronal development and functions were enriched in cases. Further analysis of 1457 human CHD probands and 2675 control subjects also revealed an enrichment of genes associated with muscle and nervous system development in patients. By combining the mice and human data, we identified a list of 101 candidate digenic genesets, from which each geneset was co-mutated in at least one mouse and two human probands with CHD but not in control mouse and control human subjects.

CONCLUSIONS

Our findings suggest that gene mutations affecting early hemodynamic perturbations in the developing heart may play a significant role as a genetic risk factor for CHD. Further validation of the candidate gene set identified in this study could enhance our understanding of the complex genetics underlying CHD and potentially lead to the development of new diagnostic and therapeutic approaches.

Thymic Atrophy and Immune Dysregulation in Infants with Complex Congenital Heart Disease

Congenital heart disease (CHD) is the most common birth defect, and up to 50% of infants with CHD require cardiovascular surgery early in life. Current clinical practice often involves thymus resection during cardiac surgery, detrimentally affecting T-cell immunity. However, epidemiological data indicate that CHD patients face an elevated risk for infections and immune-mediated diseases, independent of thymectomy. Hence, we examined whether the cardiac defect impacts thymus function in individuals with CHD. We investigated thymocyte development in 58 infants categorized by CHD complexity. To assess the relationship between CHD complexity and thymic function, we analyzed T-cell development, thymic output, and biomarkers linked to cardiac defects, stress, or inflammation. Patients with highly complex CHD exhibit thymic atrophy, resulting in low frequencies of recent thymic emigrants in peripheral blood, even prior to thymectomy. Elevated plasma cortisol levels were detected in all CHD patients, while high NT-proBNP and IL-6 levels were associated with thymic atrophy. Our findings reveal an association between complex CHD and thymic atrophy, resulting in reduced thymic output. Consequently, thymus preservation during cardiovascular surgery could significantly enhance immune function and the long-term health of CHD patients.

Systems analysis of de novo mutations in congenital heart diseases identified a protein network in the hypoplastic left heart syndrome

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.