Proteomic comparison of detergent-extracted sperm proteins from bulls with different fertility indexes
Bibliographic record
Abstract
Intrinsic factors such as proteins modulate the fertilising ability of male gametes. We compared detergent-extracted sperm protein composition of bulls with different fertility indexes in order to highlight putative fertility markers of sperm. Frozen semen from 23 Holstein bulls with documented fertility was used. According to their 'fertility solution' (SOL), as calculated by the Canadian dairy network, bulls were divided into four groups: high fertility (HF) (SOL>3.0; n=6), medium-HF (2.9>SOL>2.0; n=5), medium-low fertility (-2.8>SOL>-4.9; n=8) and low fertility (LF; SOL<-5.0; n=4), with a SOL=0 being the average. Triton X-100 protein extracts from ejaculated spermatozoa were subjected to two-dimensional difference gel electrophoresis, and polypeptide maps were quantitatively analysed by ImageMaster software. Nine protein spots showed significant differences between the HF and LF groups, and eight of these proteins were identified by liquid chromatography-tandem mass spectrometry. T-complex protein 1 subunits epsilon and (CCT5 and CCT8), two isoforms of epididymal sperm-binding protein E12 (ELSPBP1), proteasome subunit alpha type-6 and binder of sperm 1 (BSP1) were more expressed in the LF group than in the HF group. On the other hand, adenylate kinase isoenzyme 1 (AK1) and phosphatidylethanolamine-binding protein 1 (PEBP1) were more expressed in the HF group than in the LF group. The presence and expression level of ELSPBP1, BSP1, AK1 and PEBP1 were confirmed by western blot. A linear regression model established that CCT5 and AK1 explained 64% (P<0.001) of the fertility scores. The reported functions of these proteins are in agreement with a putative involvement in defective sperm physiology, where lower or higher levels can jeopardise sperm ability to reach and fertilise the oocyte.
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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".