Simulated Microgravity Reduces Focal Adhesions and Alters Cytoskeleton and Nuclear Positioning Leading to Enhanced Apoptosis via Suppressing FAK/RhoA-Mediated mTORC1/NF-κB and ERK1/2 Pathways
Bibliographic record
Abstract
Simulated-microgravity (SMG) promotes cell-apoptosis. We demonstrated that SMG inhibited cell proliferation/metastasis via FAK/RhoA-regulated mTORC1 pathway. Since mTORC1, NF-κB, and ERK1/2 signaling are important in cell apoptosis, we examined whether SMG-enhanced apoptosis is regulated via these signals controlled by FAK/RhoA in BL6-10 melanoma cells under clinostat-modelled SMG-condition. We show that SMG promotes cell-apoptosis, alters cytoskeleton, reduces focal adhesions (FAs), and suppresses FAK/RhoA signaling. SMG down-regulates expression of mTORC1-related Raptor, pS6K, pEIF4E, pNF-κB, and pNF-κB-regulated Bcl2, and induces relocalization of pNF-κB from the nucleus to the cytoplasm. In addition, SMG also inhibits expression of nuclear envelope proteins (NEPs) lamin-A, emerin, sun1, and nesprin-3, which control nuclear positioning, and suppresses nuclear positioning-regulated pERK1/2 signaling. Moreover, rapamycin, the mTORC1 inhibitor, also enhances apoptosis in cells under 1 g condition via suppressing the mTORC1/NF-κB pathway. Furthermore, the FAK/RhoA activator, toxin cytotoxic necrotizing factor-1 (CNF1), reduces cell apoptosis, restores the cytoskeleton, FAs, NEPs, and nuclear positioning, and converts all of the above SMG-induced changes in molecular signaling in cells under SMG. Therefore, our data demonstrate that SMG reduces FAs and alters the cytoskeleton and nuclear positioning, leading to enhanced cell apoptosis via suppressing the FAK/RhoA-regulated mTORC1/NF-κB and ERK1/2 pathways. The FAK/RhoA regulatory network may, thus, become a new target for the development of novel therapeutics for humans under spaceflight conditions with stressed physiological challenges, and for other human diseases.
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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".