Identification of Rat Cysteine-Rich Secretory Protein 4 (Crisp4) as the Ortholog to Human CRISP1 and Mouse Crisp4
Bibliographic record
Abstract
Cysteine-rich secretory proteins (CRISPs) are present in a diverse population of organisms and are defined by 16 conserved cysteine residues spanning a plant pathogenesis related-1 and a C-terminal cysteine-rich domain. To date, the diversification of mammalian CRISPs is evidenced by the existence of two, three, and four paralogous genes in the rat, human, and mouse, respectively. The current study identifies a third rat Crisp paralog we term Crisp4. The gene for Crisp4 is on rat chromosome 9 within 1 Mb of both the Crisp1 and Crisp2 genes. The full-length transcript for this gene was cloned from rat epididymal RNA and encodes a protein that shares 69% and 91% similarity with human CRISP1 and mouse CRISP4, respectively. Expression of rat Crisp4 is most abundant in the epididymis, with the highest levels of transcription observed in the caput and corpus epididymis. In contrast, rat CRISP4 protein is most abundant in the corpus and cauda regions of the epididymis. Rat CRISP4 protein is also present in caudal sperm extracts, appearing as a detergent-soluble form at the predicted MWR (26 kDa). Our data identify rat Crisp4 as the true ortholog to human CRISP1 and mouse Crisp4, and demonstrate its interaction with spermatozoa in the epididymis.
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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".