Selection constrains high rates of satellite DNA mutation in <i>Daphnia pulex</i>
Bibliographic record
Abstract
Abstract A long-standing evolutionary puzzle is that all eukaryotic genomes contain large amounts of tandemly-repeated satellite DNA whose composition varies greatly among even closely related species. To elucidate the evolutionary forces governing satellite dynamics, quantification of the rates and patterns of mutations in satellite DNA copy number and tests of its selective neutrality are necessary. Here we used whole-genome sequences of 28 mutation accumulation (MA) lines of Daphnia pulex in addition to six isolates from a non-MA population originating from the same progenitor to both estimate mutation rates of abundances of satellite sequences and evaluate the selective regime acting upon them. We found that mutation rates of individual satellite sequence “kmers” were both high and highly variable, ranging from additions/deletions of 0.29 – 105 copies per generation (reflecting changes of 0.12 - 0.80 percent per generation). Our results also provide evidence that new kmer sequences are often formed from existing ones. The non-MA population isolates showed a signal of either purifying or stabilizing selection, with 33 % lower variation in kmer abundance on average than the MA lines, although the level of selective constraint was not evenly distributed across all kmers. The changes between many pairs of kmers were correlated, and the pattern of correlations was significantly different between the MA lines and the non-MA population. Our study demonstrates that kmer sequences can experience extremely rapid evolution in abundance, which can lead to high levels of divergence in genome-wide satellite DNA composition between closely related species.
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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) | 0.001 | 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".