Transcriptome Sequencing of Patients With Hypertrophic Cardiomyopathy Reveals Novel Splice-Altering Variants in <i>MYBPC3</i>
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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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".