Transcriptome Sequencing of Patients With Hypertrophic Cardiomyopathy Reveals Novel Splice-Altering Variants in <i>MYBPC3</i>
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Bibliographic record
Abstract
Background: Transcriptome sequencing can improve genetic diagnosis of Mendelian diseases but requires access to tissue expressing disease-relevant transcripts. We explored genetic testing of hypertrophic cardiomyopathy using transcriptome sequencing of patient-specific human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs). We also explored whether antisense oligonucleotides (AOs) could inhibit aberrant mRNA splicing in hiPSC-CMs. Methods: We derived hiPSC-CMs from patients with hypertrophic cardiomyopathy due to MYBPC3 splice-gain variants, or an unresolved genetic cause. We used transcriptome sequencing of hiPSC-CM RNA to identify pathogenic splicing and used AOs to inhibit this splicing. Results: Transcriptome sequencing of hiPSC-CMs confirmed aberrant splicing in 2 people with previously identified MYBPC3 splice-gain variants (c.1090+453C>T and c.1224-52G>A). In a patient with an unresolved genetic cause of hypertrophic cardiomyopathy following genome sequencing, transcriptome sequencing of hiPSC-CMs revealed diverse cryptic exon splicing due to an MYBPC3 c.1928-569G>T variant, and this was confirmed in cardiac tissue from an affected sibling. Antisense oligonucleotide treatment demonstrated almost complete inhibition of cryptic exon splicing in one patient-specific hiPSC-CM line. Conclusions: Transcriptome sequencing of patient specific hiPSC-CMs solved a previously undiagnosed genetic cause of hypertrophic cardiomyopathy and may be a useful adjunct approach to genetic testing. Antisense oligonucleotide inhibition of cryptic exon splicing is a potential future personalized therapeutic option.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it