Transcriptome Analysis of Bull Semen with Extreme Nonreturn Rate: Use of Suppression-Subtractive Hybridization to Identify Functional Markers for Fertility1
Bibliographic record
Abstract
Spermatozoa are terminally differentiated cells produced during the complex process of spermatogenesis. Although the role of their residual RNA content is still being debated, this transcriptome may represent a fingerprint of spermatogenesis quality. In the present study, we undertook differential transcript profiling of spermatozoa from fertile bulls with extreme nonreturn rates (NRRs): a low-fertile group, and a high-fertile group. Using the suppression-subtractive hybridization technique in combination with macroarray analysis, we also identified novel genes. Both extreme NRR index groups retained redundant identity, such as ribosomal and mitochondrial sequences, at a statistically significant level. An elevated number of 12S, 18S, and Large Chain R rRNA gene copies were found in low-fertile bulls and validated in spermatozoa by quantitative RT-PCR for a small cohort of bulls with known fertility index. Whereas the high-NRR library exhibited a large proportion (29%) of transcripts associated with known functions (e.g., metabolism, signal transduction, translation, glycosylation, and protein degradation), only 10% of the low-NRR sequences did. This difference is also conveyed by two other categories: 17% Bovine Genome and 48% unknown in the high-NRR library, compared with 3% and 80%, respectively, in the low-NRR library. Some of the unknown transcripts are similar to expressed sequence tags detected in the male reproductive organ of certain plants and retain homology to a putative human protein. Whereas the individual transcriptome profiles may be useful in fertility assessment, these findings also suggest cross-species conservation, could contribute to a better understanding of spermatogenesis, and provide new insights regarding idiopathic infertility.
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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".