Recurrent Evolutionary Innovations in Rodent and Primate <i>Schlafen</i> Genes
Bibliographic record
Abstract
ABSTRACT SCHLAFEN proteins are a large family of RNase-related enzymes carrying essential immune and developmental functions. Despite these important roles, Schlafen genes display varying degrees of evolutionary conservation in mammals. While this appears to influence their molecular activities, a detailed understanding of these evolutionary innovations is still lacking. Here, we used in depth phylogenomic approaches to characterize the evolutionary trajectories and selective forces shaping mammalian Schlafen genes. We traced lineage-specific Schlafen amplifications and found that recent duplicates evolved under distinct selective forces, supporting repeated sub-functionalization cycles. Codon-level natural selection analyses in primates and rodents, identified recurrent positive selection over Schlafen protein domains engaged in viral interactions. Combining crystal structures with machine learning predictions, we discovered a novel class of rapidly evolving residues enriched at the contact interface of SCHLAFEN protein dimers. Our results suggest that inter Schlafen compatibilities are under strong selective pressures and are likely to impact their molecular functions. We posit that cycles of genetic conflicts with pathogens and between paralogs drove Schlafens’ recurrent evolutionary innovations in mammals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".