DeepCAGE transcriptomics identify HOXD10 as a transcription factor regulating lymphatic endothelial responses to VEGF-C
Bibliographic record
Abstract
Lymphangiogenesis plays a crucial role during development, in cancer metastasis and in inflammation. Activation of VEGFR-3 (also known as FLT4) by VEGF-C is one of the main drivers of lymphangiogenesis, but the transcriptional events downstream of VEGFR-3 activation are largely unknown. Recently, we identified a wave of immediate early transcription factors that are upregulated in human lymphatic endothelial cells (LECs) within the first 30 to 80 min after VEGFR-3 activation. Expression of these transcription factors must be regulated by additional pre-existing transcription factors that are rapidly activated by VEGFR-3 signaling. Using transcription factor activity analysis, we identified the homeobox transcription factor HOXD10 to be specifically activated at early time points after VEGFR-3 stimulation, and to regulate expression of immediate early transcription factors, including NR4A1. Gain- and loss-of-function studies revealed that HOXD10 is involved in LECs migration and formation of cord-like structures. Furthermore, HOXD10 regulates expression of VE-cadherin, claudin-5 and NOS3 (also known as e-NOS), and promotes lymphatic endothelial permeability. Taken together, these results reveal an important and unanticipated role of HOXD10 in the regulation of VEGFR-3 signaling in lymphatic endothelial cells, and in the control of lymphangiogenesis and permeability.
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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".