Perturbation of transforming growth factor (TGF)-ß1 association with latent TGF-β binding protein yields inflammation and tumors
Bibliographic record
Abstract
Transforming growth factor-beta (TGF-beta) activity is controlled at many levels including the conversion of the latent secreted form to its active state. TGF-beta is often released as part of an inactive tripartite complex consisting of TGF-beta, the TGF-beta propeptide, and a molecule of latent TGF-beta binding protein (LTBP). The interaction of TGF-beta and its cleaved propeptide renders the growth factor latent, and the liberation of TGF-beta from this state is crucial for signaling. To examine the contribution of LTBP to TGF-beta function, we generated mice in which the cysteines that link the propeptide to LTBP were mutated to serines, thereby blocking covalent association. Tgfb1(C33S/C33S) mice had multiorgan inflammation, lack of skin Langerhans cells (LC), and a shortened lifespan, consistent with decreased TGF-beta1 levels. However, the inflammatory response and decreased lifespan were not as severe as observed with Tgfb1(-/-) animals. Tgfb1(C33S/C33S) mice exhibited decreased levels of active TGF-beta1, decreased TGF-beta signaling, and tumors of the stomach, rectum, and anus. These data suggest that the association of LTBP with the latent TGF-beta complex is important for proper TGF-beta1 function and that Tgfb1(C33S/C33S) mice are hypomorphs for active TGF-beta1. Moreover, although mechanisms exist to activate latent TGF-beta1 in the absence of LTBP, these mechanisms are not as efficient as those that use the latent complex containing LTBP.
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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 | 0.000 | 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 | 0.001 |
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".