Neuropilin-1 is a receptor for transforming growth factor β-1, activates its latent form, and promotes regulatory T cell activity
Bibliographic record
Abstract
Neuropilin-1 (Nrp1) is a multifunctional protein, identified principally as a receptor for the class 3 semaphorins and members of the vascular endothelial growth factor (VEGF) family, but it is capable of other interactions. It is a marker of regulatory T cells (Tr), which often carry Nrp1 and latency-associated peptide (LAP)-TGF-beta1 (the latent form). The signaling TGF-beta1 receptors bind only active TGF-beta1, and we hypothesized that Nrp1 binds the latent form. Indeed, we found that Nrp1 is a high-affinity receptor for latent and active TGF-beta1. Free LAP, LAP-TGF-beta1, and active TGF-beta1 all competed with VEGF165 for binding to Nrp1. LAP has a basic, arginine-rich C-terminal motif similar to VEGF and peptides that bind to the b1 domain of Nrp1. A C-terminal LAP peptide (QSSRHRR) bound to Nrp1 and inhibited the binding of VEGF and LAP-TGF-beta1. We also analyzed the effects of Nrp1/LAP-TGF-beta1 coexpression on T cell function. Compared with Nrp1(-) cells, sorted Nrp1+ T cells had a much greater capacity to capture LAP-TGF-beta1. Sorted Nrp1(-) T cells captured soluble Nrp1-Fc, and this increased their ability to capture LAP-TGF-beta1. Conventional CD4+CD25(-)Nrp1(-) T cells coated with Nrp1-Fc/LAP-TGF-beta1 acquired strong Tr activity. Moreover, LAP-TGF-beta was activated by Nrp1-Fc and also by a peptide of the b2 domain of Nrp1 (RKFK; similar to a thrombospondin-1 peptide). Breast cancer cells, which express Nrp1, also captured and activated LAP-TGF-beta1 in a Nrp1-dependent manner. Thus, Nrp1 is a receptor for TGF-beta1, activates its latent form, and is relevant to Tr activity and tumor 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".