Cigarette Smoke Exposure Elicits Increased Autophagy and Dysregulation of Mitochondrial Dynamics in Murine Granulosa Cells1
Bibliographic record
Abstract
Cigarette smoking is a lifestyle behavior associated with significant adverse health effects, including subfertility and premature ovarian failure. Cigarette smoke contains a number of chemicals, many of which are involved in the generation of reactive oxygen species, which can lead to apoptosis and autophagy. Autophagy is a fundamental process that removes damaged organelles and proteins through lysosomal degradation. The relevance of autophagy to toxicant-induced changes in ovarian function is largely unexplored. Previously, we reported that exposure to cigarette smoke causes follicle loss, oxidative stress, activation of the autophagy pathway, and decreased expression of manganese superoxide dismutase, which points to altered mitochondrial function. Therefore, our objective here was to test whether exposure to cigarette smoke results in the dysregulation of mitochondrial repair mechanisms leading to loss of follicles via autophagy-mediated granulosa cell death. In this study, mice were exposed to cigarette smoke or room air for 8 wk. The expression of genes and proteins of autophagy and mitochondrial repair factors was measured using quantitative real-time PCR and Western blot analysis, immunohistochemistry, and enzyme-linked immunosorbent assay. Increased expression of parkin and decreased expression of the mitofusins suggest that exposure to cigarette smoke triggers mitochondrial damage. Moreover, the autophagy cascade proteins, BECN1 and LC3, were upregulated, whereas the antagonist BCL2 was downregulated, following treatment. Taken together, our results suggest exposure to cigarette smoke induces dysfunction of mitochondrial repair mechanisms, leading to autophagy-mediated follicle death.
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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".