Rapamycin reduces fibroblast proliferation without causing quiescence and induces STAT5A/B-mediated cytokine production
Bibliographic record
Abstract
Rapamycin is a well-known inhibitor of the Target of Rapamycin (TOR) signaling cascade; however, the impact of this drug on global genome function and organization in normal primary cells is poorly understood. To explore this impact, we treated primary human foreskin fibroblasts with rapamycin and observed a decrease in cell proliferation without causing cell death. Upon rapamycin treatment chromosomes 18 and 10 were repositioned to a location similar to that of fibroblasts induced into quiescence by serum reduction. Although similar changes in positioning occurred, comparative transcriptome analyses demonstrated significant divergence in gene expression patterns between rapamycin-treated and quiescence-induced fibroblasts. Rapamycin treatment induced the upregulation of cytokine genes, including those from the Interleukin (IL)-6 signaling network, such as IL-8 and the Leukemia Inhibitory Factor (LIF), while quiescent fibroblasts demonstrated up-regulation of genes involved in the complement and coagulation cascade. In addition, genes significantly up-regulated by rapamycin treatment demonstrated increased promoter occupancy of the transcription factor Signal Transducer and Activator of Transcription 5A/B (STAT5A/B). In summary, we demonstrated that the treatment of fibroblasts with rapamycin decreased proliferation, caused chromosome territory repositioning and induced STAT5A/B-mediated changes in gene expression enriched for cytokines.
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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".