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Record W4414367781 · doi:10.1093/gbe/evaf172

Recurrent Evolutionary Innovations in Rodent and Primate <i>Schlafen</i> Genes

2025· article· en· W4414367781 on OpenAlexfundno aff
Joris Mordier, Marine Fraisse, Michel Cohen‐Tannoudji, Antoine Molaro

Bibliographic record

VenueGenome Biology and Evolution · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicvaccines and immunoinformatics approaches
Canadian institutionsnot available
FundersInstitute of GeneticsFondation pour la Recherche MédicaleAgence Nationale de la Recherche
KeywordsHuman evolutionary geneticsNatural selectionSubfunctionalizationGeneMolecular evolutionConvergent evolutionSelection (genetic algorithm)HominidaeSexual selectionBiological evolution

Abstract

fetched live from OpenAlex

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 subfunctionalization cycles. Codon-level natural selection analyses in primates and rodents identified recurrent positive selection over Schlafen protein domains engaged in viral interactions. Combining known crystal structures and predicted protein structures, 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.248
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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