Activation of VEGFR-2 in Hematopoietic Progenitor Cells Leads to Accumulation of Myeloid Cells through Up-Regulation of GM-CSF.
Bibliographic record
Abstract
Abstract VEGF is a secreted growth factor that mediates its biological effects by binding to two transmembrane tyrosine kinase receptors, VEGFR-1 and VEGFR-2. VEGF has previously been shown to be critical in the establishment of cells of the hematopoietic and endothelial lineages. VEGF and its receptors are expressed on subsets of hematopoietic cells, and are also expressed in some hematopoietic malignancies. However, the requirement for each of the VEGF receptors, and whether the effects of VEGF are due to autocrine or paracrine signals remains unclear. We have designed a strategy that allows us to specifically study the unique effects of VEGFR-2 signaling in hematopoietic cells in vivo. By using a VEGFR-2 cytoplasmic domain fusion protein that can be dimerized with a synthetic drug, we were able to specifically study the effects of VEGFR-2 signaling in isolation. Using mice transplanted with bone marrow transduced with the inducible VEGFR-2 fusion construct, we demonstrate an expansion of myeloid progenitors by CFC assay, as well as increases in Gr-1+ and CD11b+ cells in the bone marrow. VEGFR-2 activation did not affect the proportion of lymphoid and erythroid cells. We show, by RT-PCR and ELISA, that VEGFR-2 activation can up-regulate granulocyte/macrophage-colony stimulating factor (GM-CSF) in bone marrow cells. Using activation and blocking experiments, we show that the VEGFR-2 activity is due to autocrine effects as well paracrine effects through the induction of GM-CSF in bone marrow cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".