Modeling by disruption and a selected-for partner for the nude locus
Bibliographic record
Abstract
A long-standing problem in biology is how to dissect traits for which no tractable model exists. Here, we screen for genes like the nude locus (Foxn1)—genes central to mammalian hair and thymus development—using animals that never evolved hair, thymi, or Foxn1. Fruit flies are morphologically disrupted by the FOXN1 transcription factor and rescued by weak reductions in fly gene function, revealing molecules that potently synergize with FOXN1 to effect dramatic, chaotic change. Strong synergy/effectivity in flies is expected to reflect strong selection/functionality (purpose) in mammals; the more disruptive a molecular interaction is in alien contexts (flies), the more beneficial it will be in its natural, formative contexts (mammals). The approach identifies Aff4 as the first nude-like locus, as murine AFF4 and FOXN1 cooperatively induce similar cutaneous/thymic phenotypes, similar gene expression programs, and the same step of transcription, pre-initiation complex formation. These AFF4 functions are unexpected, as AFF4 also serves as a scaffold in common transcriptional-elongation complexes. Most likely, the approach works because an interaction's power to disrupt is the inevitable consequence of its selected-for power to benefit. This study reports a simple way to dissect the molecular basis of traits when the traits evolved in organisms difficult to study. The approach identifies selected-for interactions, the molecular interactions shaped and preserved by positive selection during trait evolution, and here uncovers an essential transcriptional activator of a uniquely mammalian developmental mechanism. A genetic screen in Drosophila for genes that synergize with mammalian FOXN1 identifies Aff4 as a nude-like locus. AFF4 cooperates with FOXN1 in mammalian hair and thymus development.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".