“OMICS” of Human Sperm: Profiling Protein Phosphatases
Bibliographic record
Abstract
Phosphorylation is a major regulatory mechanism in eukaryotic cells performed by the concerted actions of kinases and phosphatases (PPs). Protein phosphorylation has long been relevant to sperm physiology, from acquisition of motility in the epididymis to capacitation in the female reproductive tract. While the precise kinases involved in the regulation of sperm phosphorylation have been studied for decades, the PPs have only recently received research interest. Tyrosine phosphorylation was first implicated in the regulation of several sperm-related functions, from capacitation to oocyte binding. Only afterwards, in 1996, the inhibition of the serine/threonine-PP phosphoprotein phosphatase 1 (PPP1) by okadaic acid and calyculin-A was shown to initiate motility in caput epididymal sperm. Today, the current mechanisms of sperm motility acquisition based on PPP1 and its regulators are still far from being fully understood. PPP1CC2, specifically expressed in mammalian sperm, has been considered to be the only sperm-specific serine/threonine-PP, while other PPP1 isoforms were thought to be absent from sperm. This article examines the "Omics" of human sperm, and reports, for the first time, the identification of three new serine/threonine-protein PPs, PPP1CB, PPP4C, and PPP6C, in human sperm, together with two tyrosine-PPs, MKP1 and PTP1C. We specifically localized in sperm PPP1CB and PPP1CC2 from the PPP1 subfamily, and PPP2CA, PPP4C, and PPP6C from the PPP2 subfamily of the serine/threonine-PPs. A semi-quantitative analysis was performed to determine the various PPs' differential expression in sperm head and tail. These findings contribute to a comprehensive understanding of human sperm PPs, and warrant further research for their clinical and therapeutic significance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".