HIF2α inhibits glutaminase clustering in mitochondria to sustain growth of clear cell renal cell carcinoma
Bibliographic record
Abstract
Clear cell renal cell carcinomas (ccRCCs) are largely driven by HIF2α and are avid consumers of glutamine. However, inhibitors of glutaminase 1 (GLS1), the first step in glutaminolysis, have not shown benefit in phase III trials, and HIF2α inhibition, recently FDA approved for treatment of ccRCC, shows significant but incomplete benefits. This highlights the need to better understand the interplay between glutamine metabolism and HIF2α in ccRCC. Here, we report that glutamine deprivation rapidly redistributed GLS1 into isolated clusters within mitochondria in diverse cell types, but not in ccRCC. GLS1 clustering occurred rapidly within 1-3 hours, was reversible, was specifically triggered by reduced intracellular glutamate, and was dependent on mitochondrial fission. Clustered GLS1 markedly enhanced glutaminase activity and promoted cell death under glutamine-deprived conditions. HIF2α prevented GLS1 clustering, independently of its transcriptional activity, thereby maintaining low GLS activity and protecting ccRCC cells from glutamine-deprivation-induced cell death. Forced clustering of GLS1, using constitutively clustering mutants, restored high GLS activity, promoted apoptosis, and suppressed ccRCC tumor growth in vivo. These findings reveal multiple insights into cellular glutamine handling, including a previously unrecognized process by which HIF2α promotes ccRCC: by suppressing GLS1 clustering and maintaining low GLS activity. This mechanism provides a potential explanation for the lack of clinical efficacy of GLS inhibitors in ccRCC and suggests a therapeutic avenue to combine HIF2α inhibition with strategies that restore GLS1 clustering.
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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".