Mammalian Transcription in Support of Hybrid mRNA and Protein Synthesis in Testis and Lung
Bibliographic record
Abstract
Post-transcriptional mechanisms including differential splicing expand the protein repertoire beyond that provided by the one gene-one protein model. Trans-splicing has been observed in mammalian systems but is low level (sometimes referred to as noise), and a contribution to hybrid protein expression is unclear. In the study of rat sperm tail proteins a cDNA, called 1038, was isolated representing a hybrid mRNA derived in part from the ornithine decarboxylase antizyme 3 (Oaz3) gene located on rat chromosome 2 fused to sequences encoded by a novel gene on chromosome 4. Cytoplasmic Oaz3 mRNA is completely testis specific. However, in several tissues Oaz3 is transcribed and contributes to hybrid 1038 mRNA synthesis, without concurrent Oaz3 mRNA synthesis. 1038 mRNA directs synthesis of a hybrid 14-kDa protein, part chromosome 2- and part chromosome 4-derived as shown in vitro and in transfected cells. Antisera that recognize a chromosome 4-encoded C-terminal peptide confirm the hybrid character of endogenous 14-kDa protein and its presence in sperm tail structures and 1038-positive tissue. Our data suggest that the testis-specific OAZ3 gene may be an example of a mammalian gene that in several tissues is transcribed to contribute to a hybrid mRNA and protein. This finding expands the repertoire of known mechanisms available to cells to generate proteome diversity.
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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".