Aging and Luteinizing Hormone Effects on Reactive Oxygen Species Production and DNA Damage in Rat Leydig Cells1
Bibliographic record
Abstract
We observed previously that after long-term suppression of luteinizing hormone (LH) and thus of Leydig cell steroidogenesis, restimulation of the Leydig cells by LH resulted in significantly higher testosterone production than by age-matched cells from control rats. These studies suggest that stimulation over time may elicit harmful effects on the steroidogenic machinery, perhaps through alteration of the intracellular oxidant-to-antioxidant balance. Herein we compared the effects of LH stimulation on stress response genes, formation of intracellular reactive oxygen species (ROS), and ROS-induced damage to ROS-susceptible macromolecules (DNA) in young and in aged cells. Microarray analysis indicated that LH stimulation resulted in significant increases in expression of genes associated with stress response and antiapoptotic pathways. Short-term LH treatment of primary Leydig cells isolated from young rats resulted in transiently increased ROS levels compared to controls. Aged Leydig cells also showed increased ROS soon after LH stimulation. However, in contrast to the young cells, ROS production peaked later and the time to recovery was increased. In both young and aged cells, treatment with LH resulted in increased levels of DNA damage but significantly more so in the aged cells. DNA damage levels in response to LH and the levels of intracellular ROS were highly correlated. Taken together, these results indicate that LH stimulation causes increased ROS production by young and aged Leydig cells and that while DNA damage occurs in cells of both ages, there is greater damage in the aged cells.
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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".