A Kinesin Is Present at Unique Sertoli/Spermatid Adherens Junctions in Rat and Mouse Testes1
Bibliographic record
Abstract
During spermatogenesis, spermatids undergo a "down and up" translocation event in the seminiferous epithelium. This event has been proposed to result from the movement of ectoplasmic specializations, which are formed in Sertoli cells at sites of adhesion to spermatids, along adjacent microtubule tracts. To test the hypothesis that a kinesin is associated with ectoplasmic specializations, we generated antibodies to conserved kinesin sequences and detected kinesins on fixed frozen testis sections and fixed seminiferous epithelial fragments. The antibodies reacted with ectoplasmic specializations related to spermatids, in addition to reacting with other structures in the epithelium known to contain kinesins. At the electron microscopy level, the antibodies reacted with the cytoplasmic face of the endoplasmic reticulum component of ectoplasmic specializations. Based on mRNA transcript screens using mouse GeneChip arrays of testis and Sertoli cells, we identified KIF20 as a candidate kinesin at ectoplasmic specializations. Antibodies generated against a peptide sequence unique to this kinesin reacted at ectoplasmic specializations in testis sections and epithelial fragments, as well as with the endoplasmic reticulum component of ectoplasmic specializations when analyzed by electron microscopy. The antibody reacted on Western blots with full-length KIF20. On Western blots of testis lysates, the antibody reacted with a protein that is not present in other tissues and which migrates at a higher molecular weight than that predicted for KIF20. Our results demonstrate that a kinesin is associated with apical ectoplasmic specializations in Sertoli cells and that the motor may be an isoform of KIF20.
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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) | 0.002 | 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".