Interaction Proteomics Identify NEURL4 and the HECT E3 Ligase HERC2 as Novel Modulators of Centrosome Architecture
Bibliographic record
Abstract
Centrosomes are composed of a centriole pair surrounded by an intricate proteinaceous matrix referred to as pericentriolar material. Although the mechanisms underpinning the control of centriole duplication are now well understood, we know relatively little about the control of centrosome size and shape. Here we used interaction proteomics to identify the E3 ligase HERC2 and the neuralized homologue NEURL4 as novel interaction partners of the centrosomal protein CP110. Using high resolution imaging, we find that HERC2 and NEURL4 localize to the centrosome and that interfering with their function alters centrosome morphology through the appearance of aberrant filamentous structures that stain for a subset of pericentriolar material proteins including pericentrin and CEP135. Using an RNA interference-resistant transgene approach in combination with structure-function analyses, we show that the association between CP110 and HERC2 depends on nonoverlapping regions of NEURL4. Whereas CP110 binding to NEURL4 is dispensable for the regulation of pericentriolar material architecture, its association with HERC2 is required to maintain normal centrosome integrity. NEURL4 is a substrate of HERC2, and together these results indicate that the NEURL4-HERC2 complex participates in the ubiquitin-dependent regulation of centrosome architecture.
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 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 | 0.001 | 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".