Differential regulation of autophagy by STAU1 in alveolar rhabdomyosarcoma and non‐transformed skeletal muscle cells
Bibliographic record
Abstract
PURPOSE: Recent work has highlighted the therapeutic potential of targeting autophagy to modulate cell survival in a variety of diseases including cancer. Recently, we found that the RNA-binding protein Staufen1 (STAU1) is highly expressed in alveolar rhabdomyosarcoma (ARMS) and that this abnormal expression promotes tumorigenesis. Here, we asked whether STAU1 is involved in the regulation of autophagy in ARMS cells. METHODS: We assessed the impact of STAU1 expression modulation in ARMS cell lines (RH30 and RH41), non-transformed skeletal muscle cells (C2C12) and STAU1-transgenic mice using complementary techniques. RESULTS: We found that STAU1 silencing reduces autophagy in the ARMS cell lines RH30 and RH41, while increasing their apoptosis. Mechanistically, this inhibitory effect was found to be caused by a direct negative impact of STAU1 depletion on the stability of Beclin-1 (BECN1) and ATG16L1 mRNAs, as well as by an indirect inhibition of JNK signaling via increased expression of Dual specificity phosphatase 8 (DUSP8). Pharmacological activation of JNK or expression silencing of DUSP8 was sufficient to restore autophagy in STAU1-depleted cells. By contrast, we found that STAU1 downregulation in non-transformed skeletal muscle cells activates autophagy in a mTOR-dependent manner, without promoting apoptosis. A similar effect was observed in skeletal muscles obtained from STAU1-overexpressing transgenic mice. CONCLUSIONS: Together, our data indicate an effect of STAU1 on autophagy regulation in ARMS cells and its differential role in non-transformed skeletal muscle cells. Our findings suggest a cancer-specific potential of targeting STAU1 for the treatment of ARMS.
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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 | 0.001 | 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".