Aging affects gene expression in spermatids of Brown Norway rats
Bibliographic record
Abstract
The effects of aging on the reproductive health of men and the consequences for their offspring are becoming more widely recognized. Correlative epidemiological studies examining paternal age and offspring health suggest there are more frequent occurrences of genetic disorders in the children of older fathers. Given the genetic basis for paternal age-related disorders, we aim to characterize gene expression in developing germ cells. Round spermatids (RS) were collected from young (mean = 5.3 months) and aged (mean = 19.5 months) Brown Norway rats, representative of humans aged 20-30 years and 55+ years, respectively. Gene expression data were obtained by mRNA sequencing (n = 5), and were analysed for differential expression. Sequencing data display 211 upregulated and 9 downregulated transcripts in RS of aged rats, compared to young (log2FC >1, p < 0.05). Transcripts with increased expression are involved in several processes including sperm motility/morphology, sperm-egg binding, capacitation, and epigenetic inheritance. In addition, there are numerous dysregulated transcripts that regulate germ cell epigenetic marks and Sertoli-germ cell binding and communication. These results show an overall increase in RS gene expression with age, with spermatogenic functions being perturbed. Taken together, these findings help identify the genetic origin of the fertility, germ cell niche, and epigenetic effects observed with advanced paternal aging.
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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 | 0.000 |
| 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".