Neuropilins bind TGF-beta and its receptor components and promote Smad signaling (88.5)
Bibliographic record
Abstract
Abstract Neuropilin-1 (Nrp1) and/or neuropilin-2 (Nrp2) are expressed by many tumors, correlating with a poor prognosis. Normally, Nrp1 is expressed by neurons, endothelial cells, dendritic cells (DCs) and Treg cells, while Nrp2 has a more limited distribution. Because neuropilins are coreceptors for VEGF, it has been thought they stimulate tumor angiogenesis, but they may also act otherwise. We recently reported that Nrp1 binds and activates LAP-TGF-beta1 (the latent form) and enhances Treg activity. Here, we report that both Nrp1 and Nrp2 interact with TGF-beta1, as well as TGF-beta receptor components, and induce internalization of the TGF-beta receptor complex. This enhances signaling by the Smad pathway. In accord with this, we found that both Nrp1 and Nrp2 activate latent TGF-beta1 on the membrane of breast cancer cells. The classical TGF-beta receptors (RI, RII and RIII) only bind active TGF-beta, but our data suggest that the neuropilins allow responsiveness to latent TGF-beta by its activation. We also find that TGF-beta competes with VEGF for binding to Nrp1/Nrp2, possibly altering angiogenesis. TGF-beta has been linked to metastasis and, thus, the neuropilins may promote metastasis by capturing and activating latent TGF-beta. Our novel findings are relevant to cancer biology, immune regulation and angiogenesis. This work was funded by the Ontario Institute for Cancer Research, Province of Ontario, Canada.
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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) | 0.006 | 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".