VEGFR1 Tyrosine Kinase Signaling Promotes Lymphangiogenesis as Well as Angiogenesis Indirectly via Macrophage Recruitment
Bibliographic record
Abstract
OBJECTIVE: Angiogenesis and lymphangiogenesis are complex phenomena that involve the interplay of several growth factors and receptors. Recently, we have demonstrated that in Keratin-14 (K14) promoter-driven Vegf-A transgenic (Tg) mice, not only angiogenesis but also lymphangiogenesis is stimulated. However, the mechanism by which VEGFR1 is involved in lymphangiogenesis remains unclear. METHODS AND RESULTS: To examine how important the tyrosine kinase (TK) of VEGFR1 is in lymphangiogenesis in K14 Vegf-A Tg mice, we crossed the K14 Vegf-A Tg mice with VEGFR1-TK-deficient mice to generate double mutant K14 Vegf-A Tg Vegfr1 tk(-/-) mice. K14 Vegf-A Tg Vegfr1 tk(-/-) mice exhibit a remarkable decrease in lymphangiogensis as well as angiogenesis in subcutaneous tissues. To address the mechanism underlying the decrease in lymphangiogensis, we investigated the recruitment of monocyte-macrophage-lineage cells into the skin. The recruitment of VEGFR1-expressing macrophages driven by VEGF-A was reduced in K14 Vegf-A Tg Vegfr1 tk(-/-) mice. Vegf-A Tg mice that received VEGFR1-TK-deficient bone marrow showed a reduction of macrophage recruitment, lymphangiogenesis and angiogenesis compared with those in K14 Vegf-A Tg mice. CONCLUSIONS: VEGFR1 signaling promotes lymphangiogenesis as well as angiogenesis mainly by increasing bone marrow-derived macrophage recruitment.
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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".