Neuropilin‐1 is a receptor for latent and active TGFβ‐1and is involved in suppression by regulatory T cells
Bibliographic record
Abstract
Neuropilin‐1 (Nrp1) is a receptor for the class 3 semaphorins and vascular endothelial growth factor (VEGF). It is also a marker of regulatory T cells (Tr), which often carry both Nrp1 and latency‐associated peptide‐TGFβ‐1. The signaling TGFβ‐1 receptors bind only active TGFβ‐1, and we hypothesized that Nrp1 binds the latent form. Indeed, we found that Nrp1 is a high affinity receptor for both latent and active TGFβ‐1. Free LAP and LAP‐TGFβ‐1 competed with VEGF165 for binding to Nrp1. LAP has a basic, arginine‐rich C‐terminal motif similar to VEGF and peptides, which bind to the b1 domain of Nrp1. A C‐terminal LAP peptide (QSSRHRR) bound to Nrp1 and inhibited the binding of VEGF and LAP‐TGFβ‐1. Compared to Nrp1‐ cells, sorted Nrp1+ T cells had a much greater capacity to capture LAP‐TGFβ‐1. Sorted Nrp1‐ T cells captured soluble Nrp1‐Fc, and this increased their ability to capture LAP‐TGFβ‐1. Conventional CD4+CD25‐Nrp1‐ T cells double‐coated with Nrp1‐Fc/LAP‐TGFβ‐1 acquired strong Tr activity. Moreover, LAP‐TGFβ‐1 was activated by Nrp1‐Fc, and also by a peptide of the b2 domain of Nrp1 (RKFK; similar to a thrombospondin‐1 KRFK peptide, which is involved in LAP‐TGFβ‐1 activation). Nrp1 also activated LAP‐TGFβ‐1 in cell‐free system. Thus, Nrp1 is a receptor for latent and active TGFβ‐1, and can contribute to Tr activity.
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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".