5S rRNA Gene Arrangements in Protists: A Case of Nonadaptive Evolution
Bibliographic record
Abstract
Given their high copy number and high level of expression, one might expect that both the sequence and organization of eukaryotic ribosomal RNA genes would be conserved during evolution. Although the organization of 18S, 5.8S and 28S ribosomal RNA genes is indeed relatively well conserved, that of 5S rRNA genes is much more variable. Here, we review the different types of 5S rRNA gene arrangements which have been observed in protists. This includes linkages to the other ribosomal RNA genes as well as linkages to ubiquitin, splice-leader, snRNA and tRNA genes. Mapping these linkages to independently derived phylogenies shows that these diverse linkages have repeatedly been gained and lost during evolution. This argues against such linkages being the primitive condition not only in protists but also in other eukaryote species. Because the only characteristic the diverse genes with which 5S rRNA genes are found linked with is that they are tandemly repeated, these arrangements are unlikely to provide any selective advantage. Rather, the observed high variability in 5S rRNA genes arrangements is likely the result of the fact that 5S rRNA genes contain internal promoters, that these genes are often transposed by diverse recombination mechanisms and that these new gene arrangements are rapidly homogenized by unequal crossingovers and/or by gene conversions events in species with short generation times and frequent founder events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".