Senescence-induced endothelial phenotypes underpin immune-mediated senescence surveillance
Bibliographic record
Abstract
Senescence is a stress-responsive tumor suppressor mechanism associated with expression of the senescence-associated secretory phenotype (SASP). Through the SASP, senescent cells trigger their own immune-mediated elimination, which if evaded leads to tumorigenesis. Senescent parenchymal cells are separated from circulating immunocytes by the endothelium, which is targeted by microenvironmental signaling. Here we show that SASP induces endothelial cell NF-κB activity and that SASP-induced endothelial expression of the canonical NF-κB component Rela underpins senescence surveillance. Using human liver sinusoidal endothelial cells (LSECs), we show that SASP-induced endothelial NF-κB activity regulates a conserved transcriptional program supporting immunocyte recruitment. Furthermore, oncogenic hepatocyte senescence drives murine LSEC NF-κB activity in vivo. Critically, we show two distinct endothelial pathways in senescence surveillance. First, endothelial-specific loss of Rela prevents development of Stat1-expressing CD4 + T lymphocytes. Second, the SASP up-regulates ICOSLG on LSECs, with the ICOS–ICOSLG axis contributing to senescence cell clearance. Our results show that the endothelium is a nonautonomous SASP target and an organizing center for immune-mediated senescence surveillance.
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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".