Proteomic markers of low and high fertility bovine spermatozoa separated by Percoll gradient
Bibliographic record
Abstract
In the context of artificial insemination, male fertility is defined as the ability to produce functional spermatozoa able to withstand cryopreservation. We hypothesized that interindividual variations in fertility depend on the proportion of the fully functional sperm population contained in the insemination dose. The objective of this study was to identify protein markers of the fully functional sperm subpopulation. Insemination doses from four high-fertility (HF) and four low-fertility (LF) bulls with comparable post-thaw quality parameters were selected for proteomic analysis using iTRAQ technology. Thawed semen was centrifuged through a Percoll gradient to segregate the motile (high density [HD]) from the immotile (low density [LD]) sperm populations. Sperm proteins were extracted with sodium deoxycholate and four groups were compared: LD and HD spermatozoa from LF and HF bulls. A total of 498 unique proteins were identified and quantified. Comparison of HD spermatozoa from HF and LF bulls revealed that five proteins were significantly more abundant in the HF group (AK8, TPI1, TSPAN8, OAT, and DBIL5) whereas five proteins were more abundant in the LF group (RGS22, ATP5J, CLU, LOC616319, and CCT5). Comparison of LD spermatozoa from HF and LF bulls revealed that four proteins were significantly more abundant in the HF group (IL4I1, CYLC2, OAT, and ARMC3) whereas 15 proteins were significantly more abundant in the LF group (HADHA, HSP90AA1, DNASE1L3, SLC25A20, GPX5, TCP1, HIP1, CLU, G5E622, LOC616319, HSPA2, NUP155, DPY19L2, SPERT, and SERPINE2). DBIL5, TSPAN8, and TPI1 showed potential as putative markers of the fully functional sperm subpopulation.
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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".