Expression of Stress Response Genes in Germ Cells During Spermatogenesis1
Bibliographic record
Abstract
During germ cell development different spermatogenic cell types show remarkable variation in their susceptibility to stressful stimuli. Various cellular mechanisms are triggered in germ cells after exposure to stress, but the expression of only a few of the genes involved in such pathways has been studied during spermatogenesis. In the present study we determined the expression profiles of 216 stress response genes in isolated rat germ cells (pachytene spermatocytes, and round and elongating spermatids) using cDNA atlas arrays. Of the 216 genes studied, 86 were detected in pachytene spermatocytes, 82 in round spermatids, and 52 in elongating spermatids. Fifty percent (48) of the total number of genes detected during spermatogenesis were detected in all three cell types while nearly 25% (25) were expressed exclusively in pachytene spermatocytes and round spermatids; some cell specific transcripts were observed also. The use of the K means clustering method allowed us to group genes by their pattern of expression during spermatogenesis; five specific expression profiles were obtained and analyzed. To determine how stress response genes are regulated throughout spermatogenesis, we examined the expression of genes involved in stress response mechanisms such as heat shock proteins-chaperones, DNA repair, and oxidative stress. Genes belonging to these families were differentially expressed during germ cell development. We suggest that the differential expression of stress response genes during spermatogenesis contributes to the selectivity of the susceptibility of germ cells to stress.
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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 | 0.001 | 0.001 |
| 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".