Spermatozoal transcriptome profiling for bull sperm motility: a potential tool to evaluate semen quality
Bibliographic record
Abstract
Regarding bull fertility, establishing an association between in vitro findings and field fertility requires a multi-parametric approach that measures the integrity of various structures and dynamic functions, such as motion characteristics, among others. The heterogeneous RNA pattern of spermatozoa could be used in genomic analysis for evaluating both spermatogenesis and fertility potential of semen, mainly because of the static status of the transcriptome of this particular differentiated cell. In a previous study, we determined that some spermatozoal transcripts identified by PCR-based cDNA subtraction are associated with non-return rate, a field fertility index. In the present study, the microarray technology was used in conjunction with differential RNA transcript extraction. We have shown that among these genes, some transcripts are also associated with the motility status of a population of sperm cells fractionated from the same ejaculate. We highlighted a systematic data analysis and validation scheme important for the identification of significant transcripts in this context. With such an approach, we found that transcripts encoding a serine/threonine testis-specific protein kinase (TSSK6) and a metalloproteinase non coding RNA (ADAM5P) are associated with high-motility status (P<0.001), also confirmed by quantitative PCR (P=0.0075). This association was found only when transcripts were extracted using the hot-TRIzol protocol, whereas the cold-TRIzol RNA extract comprised mitochondrial transcripts. These results demonstrate that some transcripts previously identified in association with field fertility are also found associated with in vitro motility provided that a stringent RNA extraction protocol is used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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".