Hypoxia-induced Changes in SUMO Conjugation Affect Transcriptional Regulation Under Low Oxygen
Bibliographic record
Abstract
Hypoxia occurs in pathological conditions, such as cancer, as a result of the imbalance between oxygen supply and consumption by proliferating cells. HIFs are critical molecular mediators of the physiological response to hypoxia but also regulate multiple steps of carcinogenesis including tumor progression and metastasis. Recent data support that sumoylation, the covalent attachment of the Small Ubiquitin-related MOdifier (SUMO) to proteins, is involved in the activation of the hypoxic response and the ensuing signaling cascade. To gain insights into differences of the SUMO1 and SUMO2/3 proteome of HeLa cells under normoxia and cells grown for 48 h under hypoxic conditions, we employed endogenous SUMO-immunoprecipitation in combination with quantitative mass spectrometry (SILAC). The group of proteins whose abundance was increased both in the total proteome and in the SUMO IPs from hypoxic conditions was enriched in enzymes linked to the hypoxic response. In contrast, proteins whose SUMOylation status changed without concomitant change in abundance were predominantly transcriptions factors or transcription regulators. Particularly interesting was transcription factor TFAP2A (Activating enhancer binding Protein 2 alpha), whose sumoylation decreased on hypoxia. TFAP2A is known to interact with HIF-1 and we provide evidence that deSUMOylation of TFAP2A enhances the transcriptional activity of HIF-1 under hypoxic conditions. Overall, these results support the notion that SUMO-regulated signaling pathways contribute at many distinct levels to the cellular response to low oxygen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".