Loss of Estrogen-Related Receptor Alpha Facilitates Angiogenesis in Endothelial Cells
Bibliographic record
Abstract
Estrogen-related receptors (ERRs) have emerged as major metabolic regulators in various tissues. However, their expression and function in the vasculature remains unknown. Here, we report the transcriptional program and cellular function of ERRα in endothelial cells (ECs), a cell type with a multifaceted role in vasculature. Of the three ERR subtypes, ECs exclusively express ERRα. Gene expression profiling of ECs lacking ERRα revealed that ERRα predominantly acts as a transcriptional repressor, targeting genes linked with angiogenesis, cell migration, and cell adhesion. ERRα-deficient ECs exhibit decreased proliferation but increased migration and tube formation. ERRα depletion increased basal as well as vascular endothelial growth factor A (VEGFA)- and ANG1/2-stimulated angiogenic sprouting in endothelial spheroids. Moreover, retinal angiogenesis is enhanced in ERRα knockout mice compared to that in wild-type mice. Surprisingly, ERRα is dispensable for the regulation of its classic targets, such as metabolism, mitochondrial biogenesis, and cellular respiration in the ECs. ERRα is enriched at the promoters of angiogenic, migratory, and cell adhesion genes. Further, VEGFA increased ERRα recruitment to angiogenesis-associated genes and simultaneously decreased their expression. Despite increasing its gene occupancy, proangiogenic stimuli decrease ERRα expression in ECs. Our work shows that endothelial ERRα plays a repressive role in angiogenesis and potentially fine-tunes growth factor-mediated angiogenesis.
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) | 0.001 | 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".