Exercise-induced extracellular vesicles mediate apoptosis in human colon cancer cells in an exercise intensity-dependent manner
Bibliographic record
Abstract
Abstract Regular exercise is known to reduce incidence rates and improve the prognosis of all cancers, but the underlying mechanisms remain elusive. Ample evidence suggests that exercise exerts therapeutic effects through extracellular vesicles (EVs), essential for cellular communication. Here, we hypothesized that exercise-induced EVs from serum of healthy young male participants will exert anti-tumorigenic effects on human colon cancer HT-29 cells, in an exercise intensity-dependent manner. 10 healthy young active males (25.4±6.2yrs, with maximal oxygen consumption (VO2max) = 45±3.7 ml.min-1.kg-1 participated in a randomized crossover trial. Participants underwent two different workload-matched, acute bouts of exercise: (1) moderate-intensity continuous exercise (MICE) at 50-55% V02max, and (2) high-intensity interval exercise (HIIE) at 90% V02max on a cycle ergometer. A control session of rest (PRE) was included. EVs were isolated from serum samples collected during PRE and immediately after each exercise session. EVs were co-incubated with colon cancer HT-29 cells (100 µg EVs/ml, 48-72h), and effect on cell viability, migration, and apoptosis measured. EV treatment reduced cell viability in all groups (PRE, MICE, HIIE) by 35%, 43% and 47% respectively, vs. PBS. EVs from HIIE group showed a significantly greater reduction in cell viability vs. PRE, therefore further analysis used these groups only. PRE-EVs reduced migration by 27%, and HIIE-EVs by 39%. EV from HIIE group increased expression of pro-apoptotic markers: Bax/Bcl-2 ratio by 56% and Caspase-3 by 30% vs. PBS, with no change observed in the PRE group. Further, 16% of cells in PRE, and 28% of cells in HIIE were TUNEL-positive, indicating DNA fragmentation, a hallmark feature of apoptosis. Our data show that exercise-induced EVs reduced cell viability, in an exercise intensity-dependent manner. HIIE-derived EVs exerted the most anti-tumorigenic effects: decreased cell viability, reduced cell migration, increase in pro-apoptotic protein expression, and elevated DNA fragmentation. It is likely these changes were mediated by altered EV Cargo induced by exercise, as the amount of EVs was the same in each treatment group. To our knowledge, this is the first human study that illustrates the therapeutic potential of exercise-induced EVs in cancer treatment.
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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".