Dynamic Nature of Cleavage Bodies and Their Spatial Relationship to DDX1 Bodies, Cajal Bodies, and Gems
Bibliographic record
Abstract
DDX1 bodies, cleavage bodies, Cajal bodies (CBs), and gems are nuclear suborganelles that contain factors involved in RNA transcription and/or processing. Although all four nuclear bodies can exist as distinct entities, they often colocalize or overlap with each other. To better understand the relationship between these four nuclear bodies, we examined their spatial distribution as a function of the cell cycle. Here, we report that whereas DDX1 bodies, CBs and gems are present throughout interphase, CPSF-100-containing cleavage bodies are predominantly found during S and G2 phases, whereas CstF-64-containing cleavage bodies are primarily observed during S phase. All four nuclear bodies associate with each other during S phase, with cleavage bodies colocalizing with DDX1 bodies, and cleavage bodies/DDX1 bodies residing adjacent to gems and CBs. Although inhibitors of RNA transcription had no effect on DDX1 bodies or cleavage bodies, inhibitors of DNA replication resulted in loss of CstF-64-containing cleavage bodies. A striking effect on nuclear structures was observed with latrunculin B, an inhibitor of actin polymerization, resulting in the formation of needlelike nuclear spicules made up of CstF-64, CPSF-100, RNA, and RNA polymerase II. Our results suggest that cleavage body components are highly dynamic in nature.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".