Long noncoding RNA GATA2-AS1 augments endothelial hypoxia inducible factor 1-α induction and regulates hypoxic signaling
Bibliographic record
Abstract
Vascular endothelial cells form the inner cellular lining of blood vessels and have myriad physiologic functions including angiogenesis and response to hypoxia. We recently identified a set of endothelial cell (EC)-enriched long noncoding RNAs (lncRNAs) in differentiated human primary cell types and described the role of the STEEL lncRNA in angiogenic patterning. We sought to further understand the role of EC-enriched lncRNAs in physiologic adaptation of the vascular endothelium. In this work, we describe an abundant, cytoplasmic, and EC-enriched lncRNA, GATA2-AS1, that is divergently transcribed from the EC-enriched developmental regulator, GATA2. While GATA2-AS1 is largely coexpressed with GATA2 in ECs, GATA2-AS1 and GATA2 appear to be complementary rather than synergistic as they have mostly distinct target genes. Common single nucleotide variants in GATA2-AS1 exons are associated with early-onset coronary artery disease and decreased expression of GATA2-AS1 in endothelial cell lines. In most cells, HIF1-α is central to the transcriptional response to hypoxia, while in ECs, both HIF1-α and HIF2-α are required to coordinate an acute and chronic response, respectively. In this setting, GATA2-AS1 contributes to the "HIF switch" and augments HIF1-α induction in acute hypoxia to regulate HIF1-α/HIF2-α balance. In hypoxia, GATA2-AS1 orchestrates HIF1-α-dependent induction of the glycolytic pathway and HIF1-α-independent maintenance of mitochondrial biogenesis. Similarly, GATA2-AS1 coordinates both metabolism and "tip/stalk" cell signaling to regulate angiogenesis in hypoxic ECs. Furthermore, we find that GATA2-AS1 expression patterns are perturbed in atherosclerotic disease. Together, these results define a role for GATA2-AS1 in the EC-specific response to hypoxia.
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 | 0.001 |
| 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".