The evolution of extremely diverged plastomes in Selaginellaceae (lycophyte) is driven by repeat patterns and the underlying <scp>DNA</scp> maintenance machinery
Bibliographic record
Abstract
Two factors are proposed to account for the unusual features of organellar genomes: the disruptions of organelle-targeted DNA replication, repair, and recombination (DNA-RRR) systems in the nuclear genome and repetitive elements in organellar genomes. Little is known about how these factors affect organellar genome evolution. The deep-branching vascular plant family Selaginellaceae is known to have a deficient DNA-RRR system and convergently evolved organellar genomes. However, we found that the plastid genome (plastome) of Selaginella sinensis has extremely accelerated substitution rates, a low GC content, pervasive repeat elements, a dynamic network structure, and it lacks direct or inverted repeats. Unexpectedly, its organelle DNA-RRR system is short of a plastid-targeted Recombinase A1 (RecA1) and a mitochondrion-targeted RecA3, in line with other explored Selaginella species. The plastome contains a large collection of short- and medium-sized repeats. Given the absence of RecA1 surveillance, we propose that these repeats trigger illegitimate recombination, accelerated mutation rates, and structural instability. The correlations between repeat quantity and architectural complexity in the Selaginella plastomes support these conclusions. We, therefore, hypothesize that the interplay of the deficient DNA-RRR system and the high repeat content has led to the extraordinary divergence of the S. sinensis plastome. Our study not only sheds new light on the mechanism of plastome divergence by emphasizing the power of cytonuclear integration, but it also reconciles the longstanding contradiction on the effects of DNA-RRR system disruption on genome structure evolution.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".