Stepwise loss of motilin and its specific receptor genes in rodents
Bibliographic record
Abstract
Specific interactions among biomolecules drive virtually all cellular functions and underlie phenotypic complexity and diversity. Biomolecules are not isolated particles, but are elements of integrated interaction networks, and play their roles through specific interactions. Simultaneous emergence or loss of multiple interacting partners is unlikely. If one of the interacting partners is lost, then what are the evolutionary consequences for the retained partner? Taking advantages of the availability of the large number of mammalian genome sequences and knowledge of phylogenetic relationships of the species, we examined the evolutionary fate of the motilin (MLN) hormone gene, after the pseudogenization of its specific receptor, MLN receptor (MLNR), on the rodent lineage. We speculate that the MLNR gene became a pseudogene before the divergence of the squirrel and other rodents about 75 mya. The evolutionary consequences for the MLN gene were diverse. While an intact open reading frame for the MLN gene, which appears functional, was preserved in the kangaroo rat, the MLN gene became inactivated independently on the lineages leading to the guinea pig and the common ancestor of the mouse and rat. Gain and loss of specific interactions among biomolecules through the birth and death of genes for biomolecules point to a general evolutionary dynamic: gene birth and death are widespread phenomena in genome evolution, at the genetic level; thus, once mutations arise, a stepwise process of elaboration and optimization ensues, which gradually integrates and orders mutations into a coherent pattern.
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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) | 0.001 | 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".