Low dose IR-induced IGF-1-sCLU expression: a p53-repressed expression cascade that interferes with TGF&x00DF;1 signaling to confer survival
Bibliographic record
Abstract
Abstract To better understand tissue responses after low IR doses, we generated a reporter system using human clusterin promoter fused to firefly luciferase (hCLUp-Luc). Secretory clusterin (sCLU), an extra-cellular molecular chaperone, induced by low doses of cytotoxic agents, clears cell debris promoting survival. Low dose IR (>2 cGy) exposure induced hCLUp-Luc activity with peak levels at 96 h, consistent with endogenous sCLU levels. As doses increased (>1 Gy), sCLU induction amplitudes increased and time to peak response decreased. sCLU expression was stimulated by IGF-1, but suppressed by p53. Responses in transgenic hCLUp-Luc reporter mice after low IR doses showed that specific tissues (i.e., colon, spleen, mammary, thymus, bone marrow) of female mice induced hCLUp-Luc activity more than male mice after whole body >10 cGy. Tissue-specific, non-linear dose- and time-responses of hCLUp-Luc and endogenous sCLU levels were noted. Colon maintained homeostatic balance after 10 cGy. Bone marrow responded with delayed, but prolonged and elevated expression. Intraperitoneal administration of the &x03B1;-TGF&x00DF;1 (1D11) antibody, but not a control antibody (13C4), immediately following IR exposure abrogated CLU induction responses. Induction in vivo also correlated with Smad signaling via activated TGF&x00DF;1 after IR. Mechanistically, media with elevated sCLU levels suppressed signaling, blocked apoptosis and increased survival of TGF&x00DF;1-exposed tumor or normal cells. Thus, sCLU is a TGF-&x00DF;1-induced pro-survival, potential bystander factor expressed in certain exposed tissues that, in turn, abrogates TGF&x00DF;1 signaling and may promote wound healing and likely contributes to a pro-tumor growth microenvironment.
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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 | 0.001 |
| 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".