Hormone-regulated dynamics of mRNA distribution on ribosomes in Sertoli cells
Bibliographic record
Abstract
Abstract The effects of hormone stimulation on the cell translational profile remain poorly understood. Here, using polysome profiling combined to RNA sequencing, we analyzed the translational response to follicle-stimulating hormone (FSH) of primary rat Sertoli cells, that exhibit an active anabolic activity regulated by reproductive hormones in the male gonad. We first established that mRNA distribution to polysomes follows a bimodal pattern, with 15% of mRNAs enriched in polysomes and exhibiting high expression. Critically, this basal polysomal enrichment had a major impact on FSH-induced mRNA recruitment to the polysomes, since FSH stimulation promoted the release of polysome-enriched mRNAs, while mRNAs that were the least associated to polysomes were preferentially recruited to polysomes upon stimulation. The FSH signal did not alter the core biological functions of Sertoli cells, but shifted the proteins involved in these functions, suggesting a molecular rewiring of the FSH-induced gene expression. These findings underscore how ribosomal reallocation dynamically adapts the cellular translatome to microenvironmental changes, enabling cells to fine-tune protein production in response to external stimuli. GRAPHICAL ABSTRACT Bullet points • In Sertoli cells, most mRNAs distribute similarly between monosomes and polysomes, but a sub-population is specifically enriched in polysomes • Basal polysomal enrichment level has a major impact on FSH-induced mRNA recruitment or release from the polysomes • The FSH signal induced a global rewiring of the proteins involved in Sertoli cell basal activity • FSH-induced reassignment of ribosomes to specific mRNAs has to comply with a tightly maintained mRNA distribution landscape
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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 | 0.000 | 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) | 0.002 | 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".