Systematic mRNA interactome analysis reconceptualizes translational quiescence in bovine sperm
Bibliographic record
Abstract
Ejaculated mammalian sperm must undergo a series of biochemical changes called capacitation to gain fertilizing competence. Mounting evidence on protein synthesis during capacitation contradicts the widely accepted dogma of translational quiescence in sperm. However, mechanisms regulating mRNA translation in sperm is ambiguous, necessitating elucidation and understanding its role in enabling fertilizing competence. Here, we perform proteome analysis from bovine sperm and identify proteins involved in translation, encompassing initiation and elongation factors, ribosomal proteins, tRNA synthetase, ligase, and RNA-binding proteins (RBPs) involved in mRNA export, degradation, and binding. We further explore the mRNA-binding activity of RBPs during capacitation, identifying 48 RBPs; 13 and 8 RBPs were exclusive to fresh-uncapacitated and capacitation groups, respectively, with an overlap of 27 RBPs present in both groups. Interestingly, cytoskeletal proteins and metabolic enzymes associate differentially with mRNAs during capacitation. Since phosphorylation is a known regulatory mechanism dynamically modulating RBPs' interactions with mRNAs, we performed sperm phosphoproteome analysis, revealing few RBPs to phosphorylate during capacitation. These observations suggest that RNA-binding functions of these proteins are coupled with capacitation-associated phosphorylation events, enabling concomitant protein synthesis and fertilizing competence in sperm. These findings will assist in elucidating translational regulation of mRNA in sperm and advancing our knowledge in regulation of male fertility.
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 | 0.000 | 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 | 0.000 | 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".