Neuropilin-1 is a receptor for transforming growth factor β-1, activates its latent form, and promotes regulatory T cell activity
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it