Evolution of metazoan oxygen-sensing involved a conserved divergence of VHL affinity for HIF1α and HIF2α
Bibliographic record
Abstract
Duplication of ancestral hypoxia-inducible factor (HIF)α coincided with the evolution of vertebrate species. Paralogs HIF1α and HIF2α are the most well-known factors for modulating the cellular transcriptional profile following hypoxia. However, how the processes of natural selection acted upon the coding region of these two genes to optimize the cellular response to hypoxia during evolution remains unclear. A key negative regulator of HIFα is von Hippel-Lindau (VHL) tumour suppressor protein. Here we show that evolutionarily-relevant substitutions can modulate a secondary contact between HIF1α Met561 and VHL Phe91. Notably, HIF1α binds more tightly than HIF2α to VHL due to a conserved Met to Thr substitution observed in the vertebrate lineage. Similarly, substitution of VHL Phe91 with Tyr, as seen in invertebrate species, decreases VHL affinity for both HIF1α and HIF2α. We propose that vertebrate evolution involved a more complex hypoxia response with fine-tuned divergence of VHL affinity for HIF1α and HIF2α.
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".