Comparative rates of evolution in endosymbiotic nuclear genomes.
Bibliographic record
Abstract
BACKGROUND: The nucleomorphs associated with secondary plastids of cryptomonads and chlorarachniophytes are the sole examples of organelles with eukaryotic nuclear genomes. Although not as widespread as their prokaryotic equivalents in mitochondria and plastids, nucleomorph genomes share similarities in terms of reduction and compaction. They also differ in several aspects, not least in that they encode proteins that target to the plastid, and so function in a different compartment from that in which they are encoded. RESULTS: Here, we test whether the phylogenetically distinct nucleomorph genomes of the cryptomonad, Guillardia theta, and the chlorarachniophyte, Bigelowiella natans, have experienced similar evolutionary pressures during their transformation to reduced organelles. We compared the evolutionary rates of genes from nuclear, nucleomorph, and plastid genomes, all of which encode proteins that function in the same cellular compartment, the plastid, and are thus subject to similar selection pressures. Furthermore, we investigated the divergence of nucleomorphs within cryptomonads by comparing G. theta and Rhodomonas salina. CONCLUSION: Chlorarachniophyte nucleomorph genes have accumulated errors at a faster rate than other genomes within the same cell, regardless of the compartment where the gene product functions. In contrast, most nucleomorph genes in cryptomonads have evolved faster than genes in other genomes on average, but genes for plastid-targeted proteins are not overly divergent, and it appears that cryptomonad nucleomorphs are not presently evolving rapidly and have therefore stabilized. Overall, these analyses suggest that the forces at work in the two lineages are different, despite the similarities between the structures of their genomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".