Glucocorticoids suppress selected components of the senescence‐associated secretory phenotype
Bibliographic record
Abstract
Cellular senescence suppresses cancer by arresting the proliferation of cells at risk for malignant transformation. Recently, senescent cells were shown to secrete numerous cytokines, growth factors, and proteases that can alter the tissue microenvironment and may promote age-related pathology. To identify small molecules that suppress the senescence-associated secretory phenotype (SASP), we developed a screening protocol using normal human fibroblasts and a library of compounds that are approved for human use. Among the promising library constituents was the glucocorticoid corticosterone. Both corticosterone and the related glucocorticoid cortisol decreased the production and secretion of selected SASP components, including several pro-inflammatory cytokines. Importantly, the glucocorticoids suppressed the SASP without reverting the tumor suppressive growth arrest and were efficacious whether cells were induced to senesce by ionizing radiation or strong mitogenic signals delivered by oncogenic RAS or MAP kinase kinase 6 overexpression. Suppression of the prototypical SASP component IL-6 required the glucocorticoid receptor, which, in the presence of ligand, inhibited IL-1α signaling and NF-κB transactivation activity. Accordingly, co-treatments combining glucocorticoids with the glucocorticoid antagonist RU-486 or recombinant IL-1α efficiently reestablished NF-κB transcriptional activity and IL-6 secretion. Our findings demonstrate feasibility of screening for compounds that inhibit the effects of senescent cells. They further show that glucocorticoids inhibit selected components of the SASP and suggest that corticosterone and cortisol, two FDA-approved drugs, might exert their effects in part by suppressing senescence-associated inflammation.
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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".