Transcriptomic analysis of dystrophin RNAi knockdown reveals a central role for dystrophin in muscle differentiation and contractile apparatus organization
Bibliographic record
Abstract
BACKGROUND: Duchenne muscular dystrophy (DMD) is a fatal muscle wasting disorder caused by mutations in the dystrophin gene. DMD has a complex and as yet incompletely defined molecular pathophysiology hindering development of effective ameliorative approaches. Transcriptomic studies so far conducted on dystrophic cells and tissues suffer from non-specific changes and background noise due to heterogeneous comparisons and secondary pathologies. A study design in which a perfectly matched control cell population is used as reference for transcriptomic studies will give a much more specific insight into the effects of dystrophin deficiency and DMD pathophysiology. RESULTS: Using RNA interference (RNAi) to knock down dystrophin in myotubes from C57BL10 mice, we created a homogenous model to study the transcriptome of dystrophin-deficient myotubes. We noted significant differences in the global gene expression pattern between these myotubes and their matched control cultures. In particular, categorical analyses of the dysregulated genes demonstrated significant enrichment of molecules associated with the components of muscle cell contractile unit, ion channels, metabolic pathways and kinases. Additionally, some of the dysregulated genes could potentially explain conditions and endophenotypes associated with dystrophin deficiency, such as dysregulation of calcium homeostasis (Pvalb and Casq1), or cardiomyopathy (Obscurin, Tcap). In addition to be validated by qPCR, our data gains another level of validity by affirmatively reproducing several independent studies conducted previously at genes and/or protein levels in vivo and in vitro. CONCLUSION: Our results suggest that in striated muscles, dystrophin is involved in orchestrating proper development and organization of myofibers as contractile units, depicting a novel pathophysiology for DMD where the absence of dystrophin results in maldeveloped myofibers prone to physical stress and damage. Therefore, it becomes apparent that any gene therapy approaches for DMD should target early stages in muscle development to attain a maximum clinical benefit. With a clear and specific definition of the transcriptome of dystrophin deficiency, manipulation of identified dysregulated molecules downstream of dystrophin may lead to novel ameliorative approaches for DMD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".