Proprotein convertases promote processing of VEGF‐D, a critical step for binding the angiogenic receptor VEGFR‐2
Bibliographic record
Abstract
ABSTRACT Vascular endothelial growth factor (VEGF)‐D is a secreted glycoprotein that induces angio‐genesis and lymphangiogenesis. It consists of a central domain, containing binding sites for VEGF receptor‐2 (VEGFR‐2) and VEGFR‐3, and N‐ and C‐terminal propep‐tides. It is secreted from the cell as homodimers of the full‐length form that can be proteolytically processed to remove the propeptides. It was recently shown, using adenoviral gene delivery, that fully processed VEGF‐D induces angiogenesis in vivo , whereas full‐length VEGF‐D does not. To better understand these observations, we monitored the effect of VEGF‐D processing on receptor binding using a full‐length VEGF‐D mutant that cannot be processed. This mutant binds VEGFR‐2, the receptor signaling for angiogenesis, with ~17, 000‐fold lower affinity than mature VEGF‐D, indicating the importance of processing for interaction with this receptor. Further, we show that members of the proprotein convertase (PC) family of proteases promote VEGF‐D processing, which facilitates the VEGF‐D/VEGFR‐2 interaction. The PCs furin and PC5 promote cleavage of both propeptides, whereas PC7 promotes cleavage of the C‐terminal propeptide only. The finding that PCs promote activation of VEGF‐D and other proteins with roles in cancer such as matrix metalloproteinases, emphasizes the importance of these enzymes as potential regulators of tumor progression and metastasis.—McColl, B. K., Paavonen, K., Karnezis, T., Harris, N. C., Davydova, N., Rothacker, J., Nice, E. C., Harder, K. W., Roufail, S., Hibbs, M. L., Rogers, P. A. W., Alitalo, K., Stacker, S. A., Achen, M. G. Proprotein convertases promote processing of VEGF‐D, a critical step for binding the angiogenic receptor VEGFR‐2. FASEB J. 21, 1088–1098 (2007)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".