Repressors NFI and NFY Participate in Organ-Specific Regulation of von Willebrand Factor Promoter Activity in Transgenic Mice
Bibliographic record
Abstract
OBJECTIVE: To determine the role of repressors in cell type and organ-specific activation of von Willebrand factor (VWF) promoter sequences -487 to 247 in vivo. METHODS AND RESULTS: Activation patterns of wild-type and mutant VWF promoters (sequences -487 to 247) containing mutations in repressors nuclear factor-I (NFI)- and nuclear factor Y (NFY)-binding sites were analyzed in transgenic mice. Mutation of the NFI-binding site activated the promoter in heart and lung endothelial cells, whereas mutation of the NFY-binding site activated the promoter in kidney vasculature. Immunofluorescence analyses showed that NFIB was predominant in heart and lung endothelial cells, whereas NFIX was predominantly detected in kidney endothelial cell nuclei. By using chromatin immunoprecipitation, we demonstrated that the distal lung-specific enhancer (containing a YY1 site) of the VWF gene is brought in proximity to the NFI binding site. CONCLUSIONS: The NFI and NFY repressors contribute differentially to organ-specific regulation of the VWF promoter, and the organ-specific action of NFI may reflect its organ-specific isoform distribution. In addition, the lung-specific enhancer region of the endogenous VWF gene may inhibit NFI repressor function through chromatin looping, which can approximate the 2 regions.
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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) | 0.002 | 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".