Microvesicles Contribute to the Bystander Effect of DNA Damage
Bibliographic record
Abstract
Genotoxic treatments elicit DNA damage response (DDR) not only in cells that are directly exposed but also in cells that are not in the field of treatment (bystander cells), a phenomenon that is commonly referred to as the bystander effect (BE). However, mechanisms underlying the BE remain elusive. We report here that etoposide and ultraviolet (UV) exposure stimulate the production of microvesicles (MVs) in DU145 prostate cancer cells. MVs isolated from UV-treated DU145 and A431 epidermoid carcinoma cells as well as etoposide-treated DU145 cells induced phosphorylation of ataxia-telangiectasia mutated (ATM) at serine 1981 (indicative of ATM activation) and phosphorylation of histone H2AX at serine 139 (γH2AX) in naïve DU145 cells. Importantly, neutralization of MVs derived from UV-treated cells with annexin V significantly reduced the MV-associated BE activities. Etoposide and UV are known to induce DDR primarily through the ATM and ATM- and Rad3-related (ATR) pathways, respectively. In this regard, MV is likely a common source for the DNA damage-induced bystander effect. However, pre-treatment of DU145 naïve cells with an ATM (KU55933) inhibitor does not affect the BE elicited by MVs isolated from etoposide-treated cells, indicating that the BE is induced upstream of ATM actions. Taken together, we provide evidence supporting that MVs are a source of the DNA damage-induced bystander effect.
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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) | 0.001 | 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".