Differential DNA methylation and gene expression in stem cell-derived cardiomyocytes from patients with and without a history of clozapine-induced myocarditis
Bibliographic record
Abstract
Clozapine is an effective antipsychotic medication for the management of treatment-resistant schizophrenia. However, the use of clozapine is limited due to severe and sometimes fatal adverse events, including cardiac inflammation (myocarditis). To date, studies of clozapine dosing and genetic studies have not identified robust risk markers. Our study aimed to identify potential epigenetic markers for clozapine-induced myocarditis using genome-wide profiling of DNA methylation and RNA sequencing in a novel in vitro model using patient-derived cells. Induced pluripotent stem cells (iPSCs) from treatment-resistant schizophrenia patients with (case) and without (control) a history of clozapine-induced myocarditis were differentiated into beating cardiomyocytes (iPSC-CMs). These cells were exposed to clozapine at a physiologically relevant concentration (2.8 µmol/L) for 24 h. Before and after clozapine treatment, RNA from the iPSC-CMs was sequenced (RNA-seq), and DNA was assessed for methylation using the EPIC array. Our analysis revealed that hypermethylation at the promoter regions of GSTM1 and ZNF559 is associated with reduced gene expression in cases relative to controls, regardless of clozapine exposure. Additionally, hypermethylation in the gene bodies of AKAP7 and HLA-DRB1 was associated with increased expression in cases relative to controls. Conversely, hypomethylation in the gene bodies of GAL3ST3 and PDPR correlated with lowered gene expression in cases relative to controls. These findings highlight a potential involvement of DNA methylation in gene expression regulation and its putative impact on clozapine-induced myocarditis. Additional studies are warranted to validate our findings and further elucidate a potential mechanism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".