EM-mosaic detects mosaic point mutations that contribute to congenital heart disease

Genetics and etiology of congenital heart disease

Congenital heart disease (CHD) is the most common severe birth anomaly, affecting almost 1% of infants. Most CHD is genetic, but only 40% of patients have an identifiable genetic risk factor for CHD. Chromosomal variation contributes significantly to CHD but is not readily amenable to biological follow-up due to the number of affected genes and lack of evolutionary synteny. The first CHD genes were implicated in extended families with syndromic CHD based on the segregation of risk alleles in affected family members. These have been complemented by more CHD gene discoveries in large-scale cohort studies. However, fewer than half of the 440 estimated human CHD risk genes have been identified, and the molecular mechanisms underlying CHD genetics remains incompletely understood. Therefore, model organisms and cell-based models are essential tools for improving our understanding of cardiac development and CHD genetic risk. Recent advances in genome editing, cell-specific genetic manipulation of model organisms, and differentiation of human induced pluripotent stem cells have recently enabled the characterization of developmental stages. In this chapter, we will summarize the latest studies in CHD genetics and the strengths of various study methodologies. We identify opportunities for future work that will continue to further CHD knowledge and ultimately enable better diagnosis, prognosis, treatment, and prevention of CHD.

In-Depth Genomic Analysis: The New Challenge in Congenital Heart Disease

The use of next-generation sequencing has provided new insights into the causes and mechanisms of congenital heart disease (CHD). Examinations of the whole exome sequence have detected detrimental gene variations modifying single or contiguous nucleotides, which are characterised as pathogenic based on statistical assessments of families and correlations with congenital heart disease, elevated expression during heart development, and reductions in harmful protein-coding mutations in the general population. Patients with CHD and extracardiac abnormalities are enriched for gene classes meeting these criteria, supporting a common set of pathways in the organogenesis of CHDs. Single-cell transcriptomics data have revealed the expression of genes associated with CHD in specific cell types, and emerging evidence suggests that genetic mutations disrupt multicellular genes essential for cardiogenesis. Metrics and units are being tracked in whole-genome sequencing studies.

Artificial intelligence-based approaches for the detection and prioritization of genomic mutations in congenital surgical diseases

Genetic mutations are critical factors leading to congenital surgical diseases and can be identified through genomic analysis. Early and accurate identification of genetic mutations underlying these conditions is vital for clinical diagnosis and effective treatment. In recent years, artificial intelligence (AI) has been widely applied for analyzing genomic data in various clinical settings, including congenital surgical diseases. This review paper summarizes current state-of-the-art AI-based approaches used in genomic analysis and highlighted some successful applications that deepen our understanding of the etiology of several congenital surgical diseases. We focus on the AI methods designed for the detection of different variant types and the prioritization of deleterious variants located in different genomic regions, aiming to uncover susceptibility genomic mutations contributed to congenital surgical disorders.

Insights into the genetic architecture of congenital heart disease from animal modeling

Congenital heart disease (CHD) is observed in up to 1% of live births and is one of the leading causes of mortality from birth defects. While hundreds of genes have been implicated in the genetic etiology of CHD, their role in CHD pathogenesis is still poorly understood. This is largely a reflection of the sporadic nature of CHD, as well as its variable expressivity and incomplete penetrance. We reviewed the monogenic causes and evidence for oligogenic etiology of CHD, as well as the role of de novo mutations, common variants, and genetic modifiers. For further mechanistic insight, we leveraged single-cell data across species to investigate the cellular expression characteristics of genes implicated in CHD in developing human and mouse embryonic hearts. Understanding the genetic etiology of CHD may enable the application of precision medicine and prenatal diagnosis, thereby facilitating early intervention to improve outcomes for patients with CHD.

Concomitant selenoenzyme inhibitor exposures as etiologic contributors to disease: Implications for preventative medicine

The physiological activities of selenium (Se) occur through enzymes that incorporate selenocysteine (Sec), a rare but important amino acid. The human genome includes 25 genes coding for Sec that employ it to catalyze challenging reactions. Selenoenzymes control thyroid hormones, calcium activities, immune responses, and perform other vital roles, but most are devoted to preventing and reversing oxidative damage. As the most potent intracellular nucleophile (pKa 5.2), Sec is vulnerable to binding by metallic and organic soft electrophiles (E*). These electron poor reactants initially form covalent bonds with nucleophiles such as cysteine (Cys) whose thiol (pKa 8.3) forms adducts which function as suicide substrates for selenoenzymes. These adducts orient E* to interact with Sec and since Se has a higher affinity for E* than sulfur, the E* transfers to Sec and irreversibly inhibits the enzyme's activity. Organic electrophiles have lower Se-binding affinities than metallic E*, but exposure sources are more abundant. Individuals with poor Se status are more vulnerable to the toxic effects of high E* exposures. The relative E*:Se stoichiometries remain undefined, but the aggregate effects of multiple E* exposures are predicted to be additive and possibly synergistic under certain conditions. The potential for the combined Se-binding effects of common pharmaceutical, dietary, or environmental E* require study, but even temporary loss of selenoenzyme activities would accentuate oxidative damage to tissues. As various degenerative diseases are associated with accumulating DNA damage, defining the effects of complementary E* exposures on selenoenzyme activities may enhance the ability of preventative medicine to support healthy aging.

Identification of Somatic Mutations From Bulk and Single-Cell Sequencing Data

Somatic mutations are DNA variants that occur after the fertilization of zygotes and accumulate during the developmental and aging processes in the human lifespan. Somatic mutations have long been known to cause cancer, and more recently have been implicated in a variety of non-cancer diseases. The patterns of somatic mutations, or mutational signatures, also shed light on the underlying mechanisms of the mutational process. Advances in next-generation sequencing over the decades have enabled genome-wide profiling of DNA variants in a high-throughput manner; however, unlike germline mutations, somatic mutations are carried only by a subset of the cell population. Thus, sensitive bioinformatic methods are required to distinguish mutant alleles from sequencing and base calling errors in bulk tissue samples. An alternative way to study somatic mutations, especially those present in an extremely small number of cells or even in a single cell, is to sequence single-cell genomes after whole-genome amplification (WGA); however, it is critical and technically challenging to exclude numerous technical artifacts arising during error-prone and uneven genome amplification in current WGA methods. To address these challenges, multiple bioinformatic tools have been developed. In this review, we summarize the latest progress in methods for identification of somatic mutations and the challenges that remain to be addressed in the future.

Molecular genetic mechanisms of congenital heart disease

Congenital heart disease (CHD) affects ~1% of all live births, but a definitive etiology is identified in only ~50%. The causes include chromosomal aneuploidies and copy-number variations, pathogenic variation in single genes, and exposure to environmental factors. High-throughput sequencing of large CHD patient cohorts and continued expansion of the complex molecular regulation of cardiac morphogenesis has uncovered numerous disease-causing genes, but the previously held monogenic model for CHD etiology does not sufficiently explain the heterogeneity and incomplete penetrance of CHD phenotypes. Here, we provide a summary of well-known genetic contributors to CHD and discuss emerging concepts supporting complex genetic mechanisms that may provide explanations for cases that currently lack a molecular diagnosis.

Exploring the Mutational Landscape of Isolated Congenital Heart Defects: An Exome Sequencing Study Using Cardiac DNA

Congenital heart defects (CHD) are the most common congenital anomalies in liveborn children. In contrast to syndromic CHD (SCHD), the genetic basis of isolated CHD (ICHD) is complex, and the underlying pathogenic mechanisms appear intricate and are incompletely understood. Next to rare Mendelian conditions, somatic mosaicism or a complex multifactorial genetic architecture are assumed for most ICHD. We performed exome sequencing (ES) in 73 parent–offspring ICHD trios using proband DNA extracted from cardiac tissue. We identified six germline de novo variants and 625 germline rare inherited variants with ‘damaging’ in silico predictions in cardiac-relevant genes expressed in the developing human heart. There were no CHD-relevant somatic variants. Transmission disequilibrium testing (TDT) and association testing (AT) yielded no statistically significant results, except for the AT of missense variants in cilia genes. Somatic mutations are not a common cause of ICHD. Rare de novo and inherited protein-damaging variants may contribute to ICHD, possibly as part of an oligogenic or polygenic disease model. TDT and AT failed to provide informative results, likely due to the lack of power, but provided a framework for future studies in larger cohorts. Overall, the diagnostic value of ES on cardiac tissue is limited in individual ICHD cases.

Clonal hematopoiesis: Mutation-specific adaptation to environmental change

Clonal hematopoiesis of indeterminate potential (CHIP) describes a widespread expansion of genetically variant hematopoietic cells that increases exponentially with age and is associated with increased risks of cancers, cardiovascular disease, and other maladies. Here, we discuss how environmental contexts associated with CHIP, such as old age, infections, chemotherapy, or cigarette smoking, alter tissue microenvironments to facilitate the selection and expansion of specific CHIP mutant clones. Further, we consider major remaining gaps in knowledge, including intrinsic effects, clone size thresholds, and factors affecting clonal competition, that will determine future application of this field in transplant and preventive medicine.

SOX7 loss-of-function variation as a cause of familial congenital heart disease

INTRODUCTION

As the most frequent type of birth defect in humans, congenital heart disease (CHD) leads to a large amount of morbidity and mortality as well as a tremendous socioeconomic burden. Accumulating studies have convincingly substantiated the pivotal roles of genetic defects in the occurrence of familial CHD, and deleterious variations in a great number of genes have been reported to cause various types of CHD. However, owing to pronounced genetic heterogeneity, the hereditary components underpinning CHD remain obscure in most cases. This investigation aimed to identify novel genetic determinants underlying CHD.

METHODS AND RESULTS

A four-generation pedigree with high incidence of autosomal-dominant CHD was enrolled from the Chinese Han race population. Using whole-exome sequencing and Sanger sequencing assays of the family members available, a novel SOX7 variation in heterozygous status, NM_031439.4: c.310C>T; p.(Gln104*), was discovered to be in co-segregation with the CHD phenotype in the whole family. The truncating variant was absent in 500 unrelated healthy subjects utilized as control individuals. Functional measurements by dual-luciferase reporter analysis revealed that Gln104*-mutant SOX7 failed to transactivate its two important target genes, GATA4 and BMP2, which are both responsible for CHD. In addition, the nonsense variation invalidated the cooperative transactivation between SOX7 and NKX2.5, which is another recognized CHD-causative gene.

CONCLUSION

The present study demonstrates for the first time that genetically defective SOX7 predisposes to CHD, which sheds light on the novel molecular mechanism underpinning CHD, and implies significance for precise prevention and personalized treatment in a subset of CHD patients.

Somatic Mutations in Cardiovascular Disease

Advances in population-scale genomic sequencing have greatly expanded the understanding of the inherited basis of cardiovascular disease (CVD). Reanalysis of these genomic datasets identified an unexpected risk factor for CVD, somatically acquired DNA mutations. In this review, we provide an overview of somatic mutations and their contributions to CVD. We focus on the most common and well-described manifestation, clonal hematopoiesis of indeterminate potential. We also review the currently available data regarding how somatic mutations lead to tissue mosaicism in various forms of CVD, including atrial fibrillation and aortic aneurism associated with Marfan Syndrome. Finally, we highlight future research directions given current knowledge gaps and consider how technological advances will enhance the discovery of somatic mutations in CVD and management of patients with somatic mutations.

Genomic frontiers in congenital heart disease

The application of next-generation sequencing to study congenital heart disease (CHD) is increasingly providing new insights into the causes and mechanisms of this prevalent birth anomaly. Whole-exome sequencing analysis identifies damaging gene variants altering single or contiguous nucleotides that are assigned pathogenicity based on statistical analyses of families and cohorts with CHD, high expression in the developing heart and depletion of damaging protein-coding variants in the general population. Gene classes fulfilling these criteria are enriched in patients with CHD and extracardiac abnormalities, evidencing shared pathways in organogenesis. Developmental single-cell transcriptomic data demonstrate the expression of CHD-associated genes in particular cell lineages, and emerging insights indicate that genetic variants perturb multicellular interactions that are crucial for cardiogenesis. Whole-genome sequencing analyses extend these observations, identifying non-coding variants that influence the expression of genes associated with CHD and contribute to the estimated ~55% of unexplained cases of CHD. These approaches combined with the assessment of common and mosaic genetic variants have provided a more complete knowledge of the causes and mechanisms of CHD. Such advances provide knowledge to inform the clinical care of patients with CHD or other birth defects and deepen our understanding of the complexity of human development. In this Review, we highlight known and candidate CHD-associated human genes and discuss how the integration of advances in developmental biology research can provide new insights into the genetic contributions to CHD.

Molecular Genetics and Complex Inheritance of Congenital Heart Disease

Congenital heart disease (CHD) is the most common congenital malformation and the leading cause of mortality therein. Genetic etiologies contribute to an estimated 90% of CHD cases, but so far, a molecular diagnosis remains unsolved in up to 55% of patients. Copy number variations and aneuploidy account for ~23% of cases overall, and high-throughput genomic technologies have revealed additional types of genetic variation in CHD. The first CHD risk genotypes identified through high-throughput sequencing were de novo mutations, many of which occur in chromatin modifying genes. Murine models of cardiogenesis further support the damaging nature of chromatin modifying CHD mutations. Transmitted mutations have also been identified through sequencing of population scale CHD cohorts, and many transmitted mutations are enriched in cilia genes and Notch or VEGF pathway genes. While we have come a long way in identifying the causes of CHD, more work is required to end the diagnostic odyssey for all CHD families. Complex genetic explanations of CHD are emerging but will require increasingly sophisticated analysis strategies applied to very large CHD cohorts before they can come to fruition in providing molecular diagnoses to genetically unsolved patients. In this review, we discuss the genetic architecture of CHD and biological pathways involved in its pathogenesis.

SOX17 loss-of-function variation underlying familial congenital heart disease

As the most prevalent form of human birth defect, congenital heart disease (CHD) contributes to substantial morbidity, mortality and socioeconomic burden worldwide. Aggregating evidence has convincingly demonstrated that genetic defects exert a pivotal role in the pathogenesis of CHD, and causative mutations in multiple genes have been causally linked to CHD. Nevertheless, CHD is of pronounced genetic heterogeneity, and the genetic components underpinning CHD in the overwhelming majority of patients remain obscure. In this research, a four-generation consanguineous family suffering from CHD transmitted in an autosomal dominant mode was recruited. By whole-exome sequencing and bioinformatics analyses as well as Sanger sequencing analyses of the family members, a new heterozygous SOX17 variation, NM_022454.4: c.553G > T; p.(Glu185*), was identified to co-segregate with CHD in the family, with complete penetrance. The nonsense variation was neither detected in 310 unrelated healthy volunteers used as controls nor retrieved in such population genetics databases as the Exome Aggregation Consortium database, Genome Aggregation Database, and the Single Nucleotide Polymorphism database. Functional assays by utilizing a dual-luciferase reporter assay system unveiled that the Glu185*-mutant SOX17 protein had no transcriptional activity on its two target genes NOTCH1 and GATA4, which have been reported to cause CHD. Furthermore, the mutation abrogated the synergistic transactivation between SOX17 and NKX2.5, another established CHD-causing transcription factor. These findings firstly indicate SOX17 loss-of-function mutation predisposes to familial CHD, which adds novel insight to the molecular mechanism of CHD, implying potential implications for genetic risk appraisal and individualized prophylaxis of the family members affected with CHD.

MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations

BACKGROUND

Mosaic mutations contribute to numerous human disorders. As such, the identification and precise quantification of mosaic mutations is essential for a wide range of research applications, clinical diagnoses, and early detection of cancers. Currently, the low-throughput nature of single allele assays (e.g., allele-specific ddPCR) commonly used for genotyping known mutations at very low alternate allelic fractions (AAFs) have limited the integration of low-level mosaic analyses into clinical and research applications. The growing importance of mosaic mutations requires a more rapid, low-cost solution for mutation detection and validation.

METHODS

To overcome these limitations, we developed Multiple Independent Primer PCR Sequencing (MIPP-Seq) which combines the power of ultra-deep sequencing and truly independent assays. The accuracy of MIPP-seq to quantifiable detect and measure extremely low allelic fractions was assessed using a combination of SNVs, insertions, and deletions at known allelic fractions in blood and brain derived DNA samples.

RESULTS

The Independent amplicon analyses of MIPP-Seq markedly reduce the impact of allelic dropout, amplification bias, PCR-induced, and sequencing artifacts. Using low DNA inputs of either 25 ng or 50 ng of DNA, MIPP-Seq provides sensitive and quantitative assessments of AAFs as low as 0.025% for SNVs, insertion, and deletions.

CONCLUSIONS

MIPP-Seq provides an ultra-sensitive, low-cost approach for detecting and validating known and novel mutations in a highly scalable system with broad utility spanning both research and clinical diagnostic testing applications. The scalability of MIPP-Seq allows for multiplexing mutations and samples, which dramatically reduce costs of variant validation when compared to methods like ddPCR. By leveraging the power of individual analyses of multiple unique and independent reactions, MIPP-Seq can validate and precisely quantitate extremely low AAFs across multiple tissues and mutational categories including both indels and SNVs. Furthermore, using Illumina sequencing technology, MIPP-seq provides a robust method for accurate detection of novel mutations at an extremely low AAF.