<i>Drosophila</i> histone locus bodies form by hierarchical recruitment of components
Bibliographic record
Abstract
Nuclear bodies are protein- and RNA-containing structures that participate in a wide range of processes critical to genome function. Molecular self-organization is thought to drive nuclear body formation, but whether this occurs stochastically or via an ordered, hierarchical process is not fully understood. We addressed this question using RNAi and proteomic approaches in Drosophila melanogaster to identify and characterize novel components of the histone locus body (HLB), a nuclear body involved in the expression of replication-dependent histone genes. We identified the transcription elongation factor suppressor of Ty 6 (Spt6) and a homologue of mammalian nuclear protein of the ataxia telangiectasia-mutated locus that is encoded by the homeotic gene multisex combs (mxc) as novel HLB components. By combining genetic manipulation in both cell culture and embryos with cytological observations of Mxc, Spt6, and the known HLB components, FLICE-associated huge protein, Mute, U7 small nuclear ribonucleoprotein, and MPM-2 phosphoepitope, we demonstrated sequential recruitment and hierarchical dependency for localization of factors to HLBs during development, suggesting that ordered assembly can play a role in nuclear body formation.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".