Neuropilin-1 exerts co-receptor function for TGF-beta-1 on the membrane of cancer cells and enhances responses to both latent and active TGF-beta
Bibliographic record
Abstract
Neuropilin (Nrp)-1 and Nrp-2 are multifunctional proteins frequently expressed by cancer cells and contribute to tumor progression by mechanisms that are not well understood. They are co-receptors for vascular endothelial growth factor and class 3 semaphorins, but recently we found that Nrp1 also binds latent and active transforming growth factor (TGF)-β1, and activates the latent form latency-associated peptide (LAP)-TGF-β1. Here, we report that Nrp1 has affinity for TGF-β receptors TβRI and TβRII, the signaling TGF-β receptors, as well as TβRIII (betaglycan), as determined in binding assays, pull down assays and confocal microscopy. Nrp1 had a higher affinity for TβRI than TβRII and could form a complex with these receptors. In breast cancer cells, Nrp1 and TβRI cointernalized in the presence of TGF-β1. Nrp1 acted as a TGF-β co-receptor by augmenting canonical Smad2/3 signaling. Importantly, Nrp-positive cancer cells, unlike negative cells, were able to activate latent TGF-β1 and respond. We examined two other membrane proteins that bind LAP-TGF-β, i.e. an RGD-binding integrin (αvβ3) and Glycoprotein A repetitions predominant (CLRRC32). RGD-binding integrins are frequently expressed by cancer cells, and glycoprotein A repetitions predominant is expressed by activated regulatory T cells that appear linked to poor tumor immunity. In vitro, these receptors did not activate LAP-TGF-β1, but subsequent addition of Nrp1 activated the cytokine. Thus, Nrp1 might collaborate with other latent TGF-β receptors in TGF-β capture and activation. We also show that Nrp2 has activities similar to Nrp1. We conclude that Nrp1 is a co-receptor for TGF-β1 and augments responses to latent and active TGF-β. Since TGF-β promotes metastasis this is highly relevant to cancer biology.
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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) | 0.002 | 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".