Doxorubicin and vincristine affect undifferentiated rat spermatogonia
Bibliographic record
Abstract
Abstract Anticancer drugs, such as alkylating agents, can affect male fertility by targeting the DNA of proliferative spermatogonial stem cells (SSC). Therefore, to reduce such side effects, other chemotherapeutics are used. However, less is known about their potential genotoxicity on SSC. Moreover, DNA repair mechanisms in SSC are poorly understood. To model treatments deprived of alkylating agents that are commonly used in cancer treatment, we tested the impact of exposure to doxorubicin and vincristine, alone or in combination (MIX), on a rat spermatogonial cell line with SSC characteristics (GC-6spg). Vincristine alone induced a cell cycle arrest and cell death without genotoxic impact. On the other hand, doxorubicin and the MIX induced a dose-dependent cell death. More importantly, doxorubicin and the MIX induced DNA breaks, measured by the COMET assay, at a non-cytotoxic dose. To elucidate which DNA repair pathway is activated in spermatogonia after exposure to doxorubicin, we screened the expression of 75 genes implicated in DNA repair. Interestingly, all were expressed constitutively in GC-6spg, suggesting great potential to respond to genotoxic stress. Doxorubicin treatments affected the expression of 16 genes (>1.5 fold change; P < 0.05) involved in cell cycle, base/nucleotide excision repair, homologous recombination and non-homologous end joining (NHEJ). The significant increase in CDKN1A and XRCC1 suggest a cell cycle arrest and implies an alternative NHEJ pathway in response to doxorubicin-induced DNA breaks. Together, our results support the idea that undifferentiated spermatogonia have the ability to respond to DNA injury from chemotherapeutic compounds and escape DNA break accumulation.
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) | 0.002 | 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".