Aerobic Exercise Alters the Melanoma Microenvironment and Modulates ERK5 S496 Phosphorylation
Bibliographic record
Abstract
Exercise changes the tumor microenvironment by remodeling blood vessels and increasing infiltration by cytotoxic immune cells. The mechanisms driving these changes remain unclear. Herein, we demonstrate that exercise normalizes tumor vasculature and upregulates endothelial expression of VCAM1 in YUMMER 1.7 and B16F10 murine models of melanoma but differentially regulates tumor growth, hypoxia, and the immune response. We found that exercise suppressed tumor growth and increased CD8+ T-cell infiltration in YUMMER but not in B16F10 tumors. Single-cell RNA sequencing and flow cytometry revealed exercise modulated the number and phenotype of tumor-infiltrating CD8+ T cells and myeloid cells. Specifically, exercise caused a phenotypic shift in the tumor-associated macrophage population and increased the expression of MHC class II transcripts. We further demonstrated that ERK5 S496A knock-in mice, which are phosphorylation deficient at the S496 residue, "mimicked" the exercise effect when unexercised, yet when exercised, these mice displayed a reversal in the effect of exercise on tumor growth and macrophage polarization compared with wild-type mice. Taken together, our results reveal tumor-specific differences in the immune response to exercise and show that ERK5 signaling via the S496 residue plays a crucial role in exercise-induced tumor microenvironment changes. See related Spotlight by Betof Warner, p. 1158.
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".