PGC-1α Promotes the Growth of ErbB2/Neu–Induced Mammary Tumors by Regulating Nutrient Supply
Bibliographic record
Abstract
Cancer cells display an increased reliance on glycolysis despite the presence of sufficient oxygen levels to support mitochondrial functions. In this study, we asked whether ameliorating mitochondrial functions in cancer cells might limit their proliferative capacity. Specifically, we increased mitochondrial metabolism in a murine cellular model of ErbB2/Neu-induced breast cancer by ectopically expressing the transcriptional coactivator peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), a master regulator of mitochondrial metabolism. As predicted, ErbB2/Neu cells ectopically expressing PGC-1α displayed an increased level of mitochondrial metabolism and reduced proliferative capacity in vitro, compared with controls. In contrast, ErbB2/Neu cells ectopically expressing PGC-1α formed larger tumors in vivo. These tumors exhibited increased concentrations of glucose and the angiogenic factor VEGF as well as higher expression of ErbB2/Neu compared with controls. We discovered that ErbB2/Neu levels were sensitive to nutrient availability, such that reduced glucose concentrations resulted in diminished ErbB2/Neu protein levels. Therefore, our data indicate that PGC-1α prevents the nutrient-mediated downregulation of ErbB2/Neu in tumors by increasing glucose supply. Mechanistic investigations revealed that the regulation of ErbB2/Neu levels by glucose was mediated by the unfolded protein response (UPR). Incubation of ErbB2/Neu-induced breast cancer cells in limited glucose concentrations or with drugs that activate the UPR led to significant reductions in ErbB2/Neu protein levels. Also, ErbB2/Neu-induced tumors ectopically expressing PGC-1α displayed lowered UPR activation compared with controls. Together, our findings uncover an unexpected link between PGC-1α-mediated nutrient availability, UPR, and ErbB2/Neu levels.
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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) | 0.001 | 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".