Origin and Evolution of Processed Pseudogenes That Stabilize Functional <i>Makorin1</i> mRNAs in Mice, Primates and Other Mammals
Bibliographic record
Abstract
We investigate the origin and evolution of a mouse processed pseudogene, Makorin1-p1, whose transcripts stabilize functional Makorin1 mRNAs. It is shown that Makorin1-p1 originated almost immediately before the musculus and cervicolor species groups diverged from each other some 4 million years ago and that the Makorin1-p1 orthologs in various Mus species are transcribed. However, Mus caroli in the cervicolor species group expresses not only Makorin1-p1, but also another older Makorin1-derived processed pseudogene, demonstrating the rapid generation and turnover in subgenus Mus. Under this circumstance, transcribed processed pseudogenes (TPPs) of Makorin1 evolved in a strictly neutral fashion even with an enhanced substitution rate at CpG dinucleotide sites. Next, we extend our analyses to rats and other mammals. It is shown that although these species also possess their own Makorin1-derived TPPs, they occur rather infrequently in simian primates. Under this circumstance, it is hypothesized that already existing TPPs must be prevented from accumulating detrimental mutations by negative selection. This hypothesis is substantiated by the presence of two rather old TPPs, MKRNP1 and MKRN4, in humans and New World monkeys. The evolutionary rate and pattern of Makorin1-derived processed pseudogenes depend heavily on how frequently they are disseminated in the genome.
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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) | 0.000 | 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".