Circulation: Genomic and Precision Medicine最新文献

Background: Vascular smooth muscle cells are key players involved in atherosclerosis, the underlying cause of coronary artery disease. They can play either beneficial or detrimental roles in lesion pathogenesis, depending on the nature of their phenotypic changes. An in-depth characterization of their gene regulatory networks can help better understand how their dysfunction may impact disease progression.

Methods: We conducted a gene expression network preservation analysis in aortic smooth muscle cells isolated from 151 multiethnic heart transplant donors cultured under quiescent or proliferative conditions.

Results: We identified 86 groups of coexpressed genes (modules) across the 2 conditions and focused on the 18 modules that are least preserved between the phenotypic conditions. Three of these modules were significantly enriched for genes belonging to proliferation, migration, cell adhesion, and cell differentiation pathways, characteristic of phenotypically modulated proliferative vascular smooth muscle cells. The majority of the modules, however, were enriched for metabolic pathways consisting of both nitrogen-related and glycolysis-related processes. Therefore, we explored correlations between nitrogen metabolism-related genes and coronary artery disease-associated genes and found significant correlations, suggesting the involvement of the nitrogen metabolism pathway in coronary artery disease pathogenesis. We also created gene regulatory networks enriched for genes in glycolysis and predicted key regulatory genes driving glycolysis dysregulation.

Conclusions: Our work suggests that dysregulation of vascular smooth muscle cell metabolism participates in phenotypic transitioning, which may contribute to disease progression, and suggests that AMT (aminomethyltransferase) and MPI (mannose phosphate isomerase) may play an important role in regulating nitrogen and glycolysis-related metabolism in smooth muscle cells.

Background: We aimed to examine clinical features and outcomes of consecutive molecularly characterized patients with Noonan syndrome with multiple lentigines and hypertrophic cardiomyopathy.

Methods: A retrospective, longitudinal multicenter cohort of consecutive children and adults with a genetic diagnosis of Noonan syndrome with multiple lentigines and hypertrophic cardiomyopathy between 2002 and 2019 was assembled. We defined a priori 3 different patterns of left ventricular remodeling during follow-up: (1) an increase in ≥15% of the maximal left ventricular wall thickness (MLVWT), both in mm and z-score (progression); (2) a reduction ≥15% of the MLVWT, both in mm and z-score (absolute regression); (3) a reduction ≥15% of the MLVWT z-score with a stable MLVWT in mm (relative regression). The primary study end point was a composite of cardiovascular death, heart transplantation, and appropriate implantable cardioverter defibrillator-shock.

Results: The cohort comprised 42 patients with Noonan syndrome with multiple lentigines and hypertrophic cardiomyopathy, with a median age at diagnosis of 3.5 (interquartile range, 0.2-12.3) years. Freedom from primary end point was 92.7% (95% CI, 84.7%-100%) 1 year after presentation and 80.9% (95% CI, 70.1%-90.7%) at 5 years. Patients with MLVWT z-score >13.7 showed reduced survival compared with those with <13.7. During a median follow-up of 3.7 years (interquartile range, 2.6-7.9), absolute regression was the most common type of left ventricular remodeling (n=9, 31%), followed by progression (n=6, 21%), and relative regression (n=6, 21%).

Conclusions: These findings provide insights into the natural history of left ventricular hypertrophy, and can help inform clinicians regarding risk stratification and clinical outcomes in patients with Noonan syndrome with multiple lentigines and hypertrophic cardiomyopathy.

Background: Restrictive cardiomyopathy in children is rare and outcomes are very poor. However, little information is available concerning genotype-outcome correlations.

Methods: We analyzed the clinical characteristics and genetic testing, including whole exome sequencing, of 28 pediatric restrictive cardiomyopathy patients who were diagnosed from 1998 to 2021 at Osaka University Hospital in Japan.

Results: The median age at diagnosis (interquartile range) was 6 (2.25-8.5) years. Eighteen patients received heart transplantations and 5 patients were on the waiting list. One patient died while waiting for transplantation. Pathologic or likely-pathogenic variants were identified in 14 of the 28 (50%) patients, including heterozygous TNNI3 missense variants in 8 patients. TNNT2, MYL2, and FLNC missense variants were also identified. No significant differences in clinical manifestations and hemodynamic parameters between positive and negative pathogenic variants were detected. However, 2- and 5-year survival rates were significantly lower in patients with pathogenic variants (50% and 22%) compared with survival in patients without pathogenic variants (62% and 54%; P=0.0496, log-rank test). No significant differences were detected in the ratio of patients diagnosed at nationwide school heart disease screening program between positive and negative pathogenic variants. Patients diagnosed by school screening showed better transplant-free survival compared with patients diagnosed by heart failure symptoms (P=0.0027 in log-rank test).

Conclusions: In this study, 50% of pediatric restrictive cardiomyopathy patients had pathogenic or likely-pathogenic gene variants, and TNNI3 missense variants were the most frequent. Patients with pathogenic variants showed significantly lower transplant-free survival compared with patients without pathogenic variants.

Background: With genetic testing advancements, the burden of incidentally identified cardiac disease-associated gene variants is rising. These variants may carry a risk of sudden cardiac death, highlighting the need for accurate diagnostic interpretation. We sought to identify pathogenic hotspots in sudden cardiac death-associated genes using amino acid-level signal-to-noise (S:N) analysis and develop a web-based precision medicine tool, DiscoVari, to improve variant evaluation.

Methods: The minor allele frequency of putatively pathogenic variants was derived from cohort-based cardiomyopathy and channelopathy studies in the literature. We normalized disease-associated minor allele frequencies to rare variants in an ostensibly healthy population (Genome Aggregation Database) to calculate amino acid-level S:N. Amino acids with S:N above the gene-specific threshold were defined as hotspots. DiscoVari was built using JavaScript ES6 and using open-source JavaScript library ReactJS, web development framework Next.js, and JavaScript runtime NodeJS. We validated the ability of DiscoVari to identify pathogenic variants using variants from ClinVar and individuals clinically evaluated at the Duke University Hospitals with cardiac genetic testing.

Results: We developed DiscoVari as an internet-based tool for S:N-based variant hotspots. Upon validation, a higher proportion of ClinVar likely pathogenic/pathogenic variants localized to DiscoVari hotspots (43.1%) than likely benign/benign variants (17.8%; P<0.0001). Further, 75.3% of ClinVar variants reclassified to likely pathogenic/pathogenic were in hotspots, compared with 41.3% of those reclassified as variants of uncertain significance (P<0.0001) and 23.4% of those reclassified as likely benign/benign (P<0.0001). Of the clinical cohort variants, 73.1% of likely pathogenic/pathogenic were in hotspots, compared with 0.0% of likely benign/benign (P<0.01).

Conclusions: DiscoVari reliably identifies disease-susceptible amino acid residues to evaluate variants by searching amino acid-specific S:N ratios.

Background: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well understood. We hypothesized that there might be a genetic basis for an AI algorithm for predicting the 5-year risk of new-onset AF using 12-lead ECGs (ECG-AI)-based risk estimates.

Methods: We applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model.

Results: In the ECG-AI GWAS, we identified 3 signals (P<5×10^-8) at established AF susceptibility loci marked by the sarcomeric gene TTN and sodium channel genes SCN5A and SCN10A. We also identified 2 novel loci near the genes VGLL2 and EXT1. In contrast, the clinical variable model prediction GWAS indicated a different genetic profile. In genetic correlation analysis, the prediction from the ECG-AI model was estimated to have a higher correlation with AF than that from the clinical variable model.

Conclusions: Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.

Background: The 2 sarcomere genes most commonly associated with hypertrophic cardiomyopathy (HCM), MYBPC3 (myosin-binding protein C3) and MYH7 (β-myosin heavy chain), are indistinguishable at presentation, and genotype-phenotype correlations have been elusive. Based on molecular and pathophysiological differences, however, it is plausible to hypothesize a different behavior in myocardial performance, impacting lifetime changes in left ventricular (LV) function.

Methods: We reviewed the initial and final echocardiograms of 402 consecutive HCM patients with pathogenic or likely pathogenic MYBPC3 (n=251) or MYH7 (n=151) mutations, followed over 9±8 years.

Results: At presentation, MYBPC3 patients were less frequently obstructive (15% versus 26%; P=0.005) and had lower LV ejection fraction compared with MYH7 (66±8% versus 68±8%, respectively; P=0.03). Both HCM patients harboring MYBPC3 and MYH7 mutations exhibited a small but significant decline in LV systolic function during follow-up; however, new onset of severe LV systolic dysfunction (LV ejection fraction, <50%) was greater among MYBPC3 patients (15% versus 5% among MYH7; P=0.013). Prevalence of grade II/III diastolic dysfunction at final evaluation was comparable between MYBPC3 and MYH7 patients (P=0.509). In a Cox multivariable analysis, MYBPC3-positive status (hazard ratio, 2.53 [95% CI, 1.09-5.82]; P=0.029), age (hazard ratio, 1.03 [95% CI, 1.00-1.06]; P=0.027), and atrial fibrillation (hazard ratio, 2.39 [95% CI, 1.14-5.05]; P=0.020) were independent predictors of severe systolic dysfunction. No statistically significant differences occurred with regard to incidence of atrial fibrillation, heart failure, appropriate implanted cardioverter defibrillator shock, or cardiovascular death.

Conclusions: MYBPC3-related HCM showed increased long-term prevalence of systolic dysfunction compared with MYH7, in spite of similar outcome. Such observations suggest different pathophysiology of clinical progression in the 2 subsets and may prove relevant for understanding of genotype-phenotype correlations in HCM.