Post-transcriptional regulation of Rbm5 expression in undifferentiated H9c2 myoblasts
Bibliographic record
Abstract
We previously examined the expression of Rbm5 during myoblast differentiation and found significantly more protein in the early stages of skeletal myoblast differentiation than during the later stages. We decided to determine if this elevated level was necessary for differentiation. Our hypothesis was that if high levels of Rbm5 protein expression were necessary for the initiation of skeletal myoblast differentiation, then inhibition of expression would prevent differentiation. Our long-term objective is to inhibit Rbm5 expression and examine the effect on H9c2 differentiation. Towards this end, stable knockdown clones and transient knockdown populations were generated. Expression analyses in H9c2 myoblasts demonstrated significant Rbm5 messenger RNA (mRNA) inhibition but, surprisingly, no effect on RBM5 protein levels. Expression of the Rbm5 paralogue Rbm10 was examined in order to (a) ensure no off-target knockdown effect, and (b) investigate any possible compensatory effects. RBM10 protein levels were found to be elevated, in both the clonal and transiently transfected populations. These results suggest that myoblast RBM5 expression is regulated by a process that includes RNA sequestration and/or controlled translation, and that (a) RBM5 function is compensated for by RBM10, and/or (b) RBM5 regulates RBM10 expression. We have developed a model to describe our findings, and suggest further experiments for testing its validity. Since upregulation of Rbm10 might compensate for downregulated Rbm5, and consequently might mask any potential knockdown effect, it could lead to incorrect conclusions regarding the importance of Rbm5 for differentiation. It is therefore imperative to determine how both RBM5 and RBM10 protein expression is regulated.
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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".