CCL5 activation of CCR5 regulates cell metabolism to enhance proliferation of breast cancer cells
Bibliographic record
Abstract
In earlier studies, we showed that CCL5 enhances proliferation and survival of MCF-7 breast cancer cells in an mTOR-dependent manner and we provided evidence that, for T cells, CCL5 activation of CCR5 results in increased glycolysis and enhanced ATP production. Increases in metabolic activity of cancer cells, specifically increased glycolytic activity and increased expression of glucose transporters, are associated with tumour progression. In this report, we provide evidence that CCL5 enhances the proliferation of human breast cancer cell lines (MDA-MB-231, MCF-7) and mouse mammary tumour cells (MMTV-PyMT), mediated by CCR5 activation. Concomitant with enhanced proliferation we show that CCL5 increases cell surface expression of the glucose transporter GLUT1, and increases glucose uptake and ATP production by these cells. Blocking CCL5-inducible glucose uptake abrogates the enhanced proliferation induced by CCL5. We provide evidence that increased glucose uptake is associated with enhanced glycolysis, as measured by extracellular acidification. Moreover, CCL5 enhances the invasive capacity of these breast cancer cells. Using metabolomics, we demonstrate that the metabolic signature of CCL5-treated primary mouse mammary tumour cells reflects increased anabolic metabolism. The implications are that CCL5-CCR5 interactions in the tumour microenvironment regulate metabolic events, specifically glycolysis, to promote tumour proliferation and invasion.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".