Predisposition to tetraploidy in pulmonary vascular smooth muscle cells derived from the Eker rats
Bibliographic record
Abstract
Somatic mutations in the tuberous sclerosis complex-2 (TSC2) gene are associated with pulmonary lymphangioleiomyomatosis (LAM), a disorder characterized by benign lesions of smooth muscle and/or smooth muscle-like cells in the lung. However, the cellular mechanisms underlying LAM disease are largely unknown. Given that the TSC2 gene product tuberin is involved in the regulation of cell growth and proliferation, the present study was designed to investigate the potential roles of TSC2 in regulation of the cell cycle. We studied cell cycle profiles of pulmonary vascular smooth muscle cells (SMCs) derived from Eker rats (Tsc2(+/EK)), a genetic model carrying a germline insertional deletion in one copy of the Tsc2 gene, and the wild-type rats (Tsc2(+/+)), a noncarrier counterpart. We found that Tsc2(+/EK), but not Tsc2(+/+), SMCs displayed increases in cells with > or =4N DNA content (> or =4N cells) and in the bromodeoxyuridine (BrdU) incorporation of > or =4N cells. Centrosome number was also increased in Tsc2(+/EK) SMCs, but the mitotic index was comparable between Tsc2(+/+) and Tsc2(+/EK) SMCs. Furthermore, Tsc2(+/EK) SMCs showed elevated phosphorylation of p70S6K and increased expression of cell cycle regulatory proteins Cdk1, cyclin B, Cdk2, and cyclin E. Inhibition of the mammalian target of rapamycin (mTOR) pathway by rapamycin not only inhibited the phosphorylation of p70(S6K) and the expression of cell cycle regulatory proteins but also reduced accumulation of > or =4N cells and BrdU incorporation of >4N cells. Therefore, our results demonstrate that Tsc2(+/EK) SMCs are predisposed to undergo tetraploidization, involving activation of the mTOR pathway. These findings suggest an important role of Tsc2 in regulation of the cell cycle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".