Seema Mital

BACKGROUND: Congenital heart disease (CHD) is the most common birth defect. Ninety percent of isolated cases remain genotype-elusive despite short-read genome sequencing (GS). Our goal was to assess genomic yield with long-read GS compared with short-read GS in CHD. METHODS: We performed Illumina short-read GS on 1101 CHD probands. In a subset of 46 genotype-elusive probands, we performed additional PacBio long-read GS as well as parental sequencing of 7 complete trios. We compared variant calls genome-wide, including across dark and CHD genes in samples with paired short-read and long-read GS. RESULTS: Paired analysis of the 46 probands revealed that genome-wide, long-read GS had 1.01- to 7.93-fold higher call rates of single nucleotide variants, deletions, duplications, and insertions but fewer indels and inversions compared with short-read GS. With a genome-wide sequencing depth of 18.3×, long-read GS had higher coverage for 10 Tier 1 CHD genes and dark genes, but nonuniform and low sequencing depth (ZEB2 in a CHD proband who had an extracardiac phenotype overlapping with Mowat-Wilson syndrome. CONCLUSIONS: Long-read GS demonstrated higher genome-wide variant call yield compared with short-read GS, better coverage of several cardiac-relevant genes, and better resolution of complex structural variants, including tandem repeat expansions. Long-read GS may provide an option for a subset of patients with CHD who remain genotype-elusive on short-read GS.

BACKGROUND: Fetal cardiomyopathy is a rare condition, often with an unknown cause and associated with high perinatal mortality. Recent years have seen improvement in fetal cardiac screening, genetic testing, and management. We sought to investigate genetic associations and clinical outcomes of fetal cardiomyopathy in the contemporary era. METHODS: A single-arm (descriptive) retrospective cohort study of fetal cardiomyopathy cases diagnosed from January 2017 to December 2021 at 26 North American centers in the Fetal Heart Society Research Collaborative was undertaken. Cases attributable to maternal diabetes, extra-cardiac conditions, structural heart disease, or arrhythmias were excluded. Genetic testing results, extra-cardiac structural anomalies, and outcomes were collected. Logistic regression was performed to determine prenatal risk factors for death or cardiac transplantation by 1-year of age. Descriptive competing-risk analyses (cumulative incidence functions) and Kaplan-Meier survival estimates were used to describe outcomes. Multivariable logistic regression with 5 prespecified covariates was performed to identify prenatal factors associated with death or cardiac transplantation by 1-year of age. RESULTS: In 148 cases of fetal cardiomyopathy, 6.7% (10/148) opted for pregnancy termination, 8.6% (12/138) of continuing pregnancies experienced fetal death, and 1.4% (2/138) were lost to follow-up. Among continuing pregnancies, overall 1-year survival was 61.5% (85/138) with 1-year transplant-free survival of 50.0% (69/138). Of live births, 8.8% (11/124) received palliative care from the outset, and 21.2% (24/113) of those with intention to treat were listed for cardiac transplant, of whom 75.0% (18/24) received transplantation by age 1-year. Genetic etiologies were found in 34.0% (50/147) of the cohort, with variants of uncertain significance detected in an additional 27.2% (40/147). Fetal hydrops at the time of prenatal diagnosis ( P =0.001) and a prenatal diagnosis of extracardiac structural anomalies ( P ≤0.001) were significantly associated with death or transplant by 1-year of age. CONCLUSIONS: Fetal cardiomyopathy outcomes have improved in the current era, although only half achieve 1-year cardiac transplant-free survival. Genetic testing identifies a cause in one-third of cases. Hydrops at the time of fetal cardiomyopathy diagnosis and a prenatal diagnosis of extra-cardiac structural anomalies remain important risk factors for mortality.

Background: Current clinical guidelines recommend serial ECG and echocardiographic surveillance for first-degree relatives of probands with Hypertrophic Cardiomyopathy (HCM). Objectives: To evaluate the accuracy and validity of ECG alone as a pre-screening tool for the diagnosis of HCM and to develop a random forest (RF) model for HCM phenotype prediction. Method: Pediatric relatives of primary HCM probands attending the cardiomyopathy screening program at The Hospital for Sick Children were included from 1993 till 2025. Subjects were followed until last follow-up censored at phenotype conversion. ECGs were classified as normal or abnormal based on predefined parameters. Associations between binary ECG variables and HCM phenotype were assessed using Phi (φ) coefficient. A Random Forest classifier was developed using significant ECG variables (70:30 training:test split) and evaluated using precision, recall, specificity, negative predictive value, F1 score and AUROC. feature importance was assessed using SHAP analysis. Variables with impact of >5% were included in a simplified model which was evaluated by repeating performance metrics and externally validated in a healthy cohort. Results: 350 screened relatives (44% female, mean follow-up 6.8 ± 4.8 years) were included. At baseline 13% (46\350) were phenotype-positive for HCM. 9 subjects converted during the surveillance. Thirteen ECG variables were significantly associated with phenotype-positive HCM and were included in the full random forest model. Four variables had >5% impact (Left ventricular hypertrophy, right ventricular hypertrophy, T-wave inversion and ST-segment depression) and were included in a simplified model which maintained high specificity (93% vs 97%), negative predictive value (97% vs 93%) and AUROC (90% vs, 96%). The simplified model classified 83% subjects as phenotype-negative with eight being false-negative, all of whom developed an abnormal ECG in a mean of 1 year and none had an interim adverse cardiac event. The simplified model was evaluated in an independent healthy cohort of 153 school age subjects and correctly identified 98% as phenotype-negative with 100% NPV. Conclusion: ECG abnormalities were strongly associated with phenotype-positive status. A simplified ECG-based random forest model using four ECG variables demonstrated high specificity and negative predictive value for identifying phenotype-negative subjects. If prospectively validated, this could reduce the need for concurrent echocardiographic screening by up to 83% per encounter, lowering screening burden and cost.

Diastolic heart failure (HF) in primary cardiomyopathy is under-recognized and often diagnosed late, particularly in children. While recent studies have advanced understanding of HF with preserved ejection fraction in older adults, the prevalence, outcomes and molecular drivers of diastolic HF in pediatric and young adult cardiomyopathy remain poorly defined, where disease is typically driven by primary myocardial disease rather than acquired co-morbidities. The Canadian Cardiomyopathy Collaborative (C3) was assembled to leverage three of Canada’s leading pediatric and adult cardiomyopathy biobank registries. Its flagship initiative, Artificial Intelligence to Model Diastolic Heart Failure (AID-HF), aims to integrate deep phenotyping - including comprehensive diastolic function assessment - with genomics, lipidomics and proteomics and apply machine learning to identify biological and clinical signatures that drive cardiac function and outcomes in cardiomyopathy. Harmonized phenotyping and multiomics protocols across registries will create a uniquely integrated national data resource and enable the goals of AID-HF i.e., earlier diagnosis and new therapeutic targets for diastolic HF in cardiomyopathy.

There are around 100 genes or copy-number variations used in genetic testing for autism spectrum disorder (ASD)^1,2. The established genes are protein coding, and the associated phenotypes usually extend beyond sociobehavioural traits seen in autism, including cognitive/medical complexities and attention deficit hyperactivity disorder (ADHD)^3,4. We examined whole-genome sequencing data in cases of ASD (9,349) and controls (8,332) and identify 27 male individuals with ASD with X-chromosome microdeletions that implicate the long non-coding RNA PTCHD1-AS as an ASD-susceptibility gene (odds ratio = 2.56, P = 0.01). Two Ptchd1-as-knockout mouse models, which were created by disrupting/deleting the evolutionarily conserved exon 3, show ASD-like features in male mice, including increased repetitive behaviours and impaired social behaviour and communication without cognitive comorbidities or ADHD-like behaviours. Hippocampus-dependent synaptic function, complex learning and locomotor activity are unaffected in knockout mice. Native nuclear-enriched mouse Ptchd1-as showed sustained expression from postnatal day 7 onwards in the dorsal striatum, a predominantly GABAergic brain region that is implicated in ASD⁵. Multi-omics analysis revealed transcriptomic alterations in striatal oligodendrocytes, astrocytes and neurons impacting myelination and synaptic plasticity. Disrupting Ptchd1-as led to reductions in conventional protein kinase C (cPKC) isoforms, altered SRC and GSK-3α/β phosphorylation and enhanced striatal synaptic plasticity (long-term potentiation and long-term depression). Together, these findings implicate striatal molecular and circuit-level dysregulation through PTCHD1-AS in ASD aetiology.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) hold tremendous promise for in vitro modeling to assess native myocardial function and disease mechanisms, as well as testing drug safety and efficacy. However, current hiPSC-CMs are functionally immature, resembling in vivo CMs of fetal or neonatal developmental states. The use of targeted culture media and organoid formats have been identified as potential high-yield contributors to improve CM maturation. This study presents an hiPSC-CM maturation medium formulation, designed using a differential evolutionary approach targeting metabolic functionality for iterative optimization. Relative to existing high-performing reference formulations, our medium significantly matured morphology, Ca²⁺ handling, electrophysiology, and metabolism, which was further validated by multi-omic screening, for cells in either pure or co-cultured microtissue formats. Together, these findings not only provide a reliable workflow for highly functional hiPSC-CMs for downstream use, but also demonstrate the power of high-dimensional optimization processes in evoking advanced biological function in vitro.

Background: Congenital heart defects (CHDs) are the most common malformation amongst newborns, with a prevalence of approximately 0.8–2%. The etiology of CHD is highly complex and can be linked to genetic and nongenetic factors. The molecular basis remains partially unclear, and only a minority of patients can be assigned to clear monogenic causes. Methods: Here we analyzed a cohort of 3907 CHD cases and population-matched controls using exome sequencing. In addition, we employed epigenetic profiling on a subset of cases that harbored rare NOTCH1 variants. Results: We identified 24 pathogenic or likely pathogenic single nucleotide variants (SNVs) in NOTCH1 in our exome cohort, as well as a further 15 variants of uncertain significance (VUS) likely to have a deleterious effect. Although the cardiac phenotypes showed some heterogeneity, non-syndromic Tetralogy of Fallot (ToF) and related malformations were the most frequent finding in 56% (22/39). In particular, missense variants altering cysteine residues involved in forming disulfide bridges were identified, specifically in TOF patients. Altogether, NOTCH1 -haploinsufficiency represented the most common monogenic cause in our cohort and accounted for an estimated 1% of CHD cases. Combined with additional cases assembled through collaborations, we present 67 individuals with ultrarare variants affecting NOTCH1 . This prominent role of NOTCH1 calls for an accurate and accessible evaluation of variants. To this end we explored DNA methylation testing and successfully established a NOTCH1 -specific episignature. This signature also displays a robust specificity in relation to 99 other episignatures. Taken together, we found that truncating, splice-altering, as well as missense NOTCH1 variants, can generate a distinct DNAm episignature. Conclusions: We identified that NOTCH1 -haploinsufficiency variants represented the most common monogenic cause in our cohort and accounted for an estimated 1 % of CHD cases. Furthermore, we conclude that methylation profiling can contribute to (NOTCH1) variant interpretation and improve the diagnostic management of CHD patients. Lastly, we established a NOTCH1 -specific episignature, which represents the first non-syndromic signature, significantly extending the scope of patients that can benefit from methylation analysis.

Background: Pediatric hypertrophic cardiomyopathy (HCM) is associated with significant morbidity and mortality. While identified as a genetic disease mainly involving sarcomeric genes, the association between genotypical variation and phenotypic expression is not fully established. Developments in Ultrafast ultrasound imaging allows quantifying myocardial stiffness using shear waves elastography (SWE). When combined with strain measurements, myocardial work can be computed, offering new insights into phenotype myocardial properties. Methods: An age-matched population of 20 Healthy volunteers (HVs, mean age=11.1 ± 4.5years), 20 HCM (genotype- and phenotype-positive, mean age=11.6 ± 5.3years) and 20 Genotype (genotype-positive, phenotype-negative, mean age=11.1 ± 4.8years) were included in the study. Each participant underwent conventional echocardiography and a full cardiac-cycle exploration of the basal anteroseptal segment consisting of: (1) myocardial stiffness by SWE, (2) segmental strain and thickness, which are used to compute one-beat work, the stress-strain loop area, contributive and dissipative work. Results: Mean diastolic myocardial stiffness (DMS) and peak myocardial strain (PMS) distinguished the HCM group (DMS=23.7 ± 8.7kPa; PMS= −6.64 ± 5.9%) from HV group (DMS=7.2 ± 0.7kPa, p<0.01; PMS= −19.9 ±4.1%, p<0.01). No significant differences were observed in DMS and PMS between HVs and Genotype groups. One-beat work and stress-strain loop areas showed significant differences among all 3 groups (p<0.01) and could distinguish the Genotype group (one-beat work= 318.2 ± 100.2µJ/mm; stress-strain loop area=33.2 ± 10.6kPa.%) from HVs (one-beat work= 582.4 ± 137µJ/mm; stress-strain loop area= 66.8 ± 19.8 kPa.%), and the HCM group (one-beat work= 38.2 ± 107.1µJ/mm, stress-strain loop area=5.8 ± 6.5kPa.%), p<0.01. Conclusion: Combining Ultrafast ultrasound with speckle-tracking echocardiography, we demonstrate that one-beat work and stress-strain relationship, obtained by combining myocardial stiffness, strain, and thickness have the potential to distinguish genotype-positive, phenotype-negative patients from healthy controls. Clinical outcome studies are needed to determine the prognostic value of these parameters in phenotype-positive HCM patients.