Higher‐order oligomerization promotes localization of SPOP to liquid nuclear speckles
Bibliographic record
Abstract
Membrane‐less organelles in cells are large, dynamic protein/protein or protein/RNA assemblies that have been reported in some cases to have liquid droplet properties. However, the molecular interactions underlying the recruitment of components are not well understood. Herein, we study how the ability to form higher‐order assemblies influences the recruitment of the speckle‐type POZ protein (SPOP) to nuclear speckles. SPOP, a cullin‐3‐RING ubiquitin ligase (CRL3) substrate adaptor, self‐associates into higher‐order oligomers; that is, the number of monomers in an oligomer is broadly distributed and can be large. While wild‐type SPOP localizes to liquid nuclear speckles, self‐association‐deficient SPOP mutants have a diffuse distribution in the nucleus. SPOP oligomerizes through its BTB and BACK domains. We show that BTB‐mediated SPOP dimers form linear oligomers via BACK domain dimerization, and we determine the concentration‐dependent populations of the resulting oligomeric species. Higher‐order oligomerization of SPOP stimulates CRL3SPOP ubiquitination efficiency for its physiological substrate Gli3, suggesting that nuclear speckles are hotspots of ubiquitination. Dynamic, higher‐order protein self‐association may be a general mechanism to concentrate functional components in membrane‐less cellular bodies. SPOP, a ubiquitin ligase substrate receptor and tumor suppressor, self‐associates indefinitely into large oligomers via the synergistic function of its tandem dimerization domains. The resulting oligomers are recruited to liquid nuclear speckles, likely generating hotspots of SPOP‐mediated ubiquitination. Self‐association of the ubiquitin ligase adaptor and tumor suppressor SPOP is required for its recruitment to liquid nuclear bodies, likely generating hotspots of SPOP‐mediated ubiquitination.
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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 | 0.000 | 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".