The evolution of mitochondrial genomes in modern frogs (Neobatrachia): nonadaptive evolution of mitochondrial genome reorganization
Bibliographic record
Abstract
BACKGROUND: Although mitochondrial (mt) gene order is highly conserved among vertebrates, widespread gene rearrangements occur in anurans, especially in neobatrachians. Protein coding genes in the mitogenome experience adaptive or purifying selection, yet the role that selection plays on genomic reorganization remains unclear. We sequence the mitogenomes of three species of Glandirana and hot spots of gene rearrangements of 20 frog species to investigate the diversity of mitogenomic reorganization in the Neobatrachia. By combing these data with other mitogenomes in GenBank, we evaluate if selective pressures or functional constraints act on mitogenomic reorganization in the Neobatrachia. We also look for correlations between tRNA positions and codon usage. RESULTS: Gene organization in Glandirana was typical of neobatrachian mitogenomes except for the presence of pseudogene trnS (AGY). Surveyed ranids largely exhibited gene arrangements typical of neobatrachian mtDNA although some gene rearrangements occurred. The correlation between codon usage and tRNA positions in neobatrachians was weak, and did not increase after identifying recurrent rearrangements as revealed by basal neobatrachians. Codon usage and tRNA positions were not significantly correlated when considering tRNA gene duplications or losses. Change in number of tRNA gene copies, which was driven by genomic reorganization, did not influence codon usage bias. Nucleotide substitution rates and dN/dS ratios were higher in neobatrachian mitogenomes than in archaeobatrachians, but the rates of mitogenomic reorganization and mt nucleotide diversity were not significantly correlated. CONCLUSIONS: No evidence suggests that adaptive selection drove the reorganization of neobatrachian mitogenomes. In contrast, protein-coding genes that function in metabolism showed evidence for purifying selection, and some functional constraints appear to act on the organization of rRNA and tRNA genes. As important nonadaptive forces, genetic drift and mutation pressure may drive the fixation and evolution of mitogenomic reorganizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".