A multifunctional, multi-pathway intracellular localization signal in Huntingtin
Bibliographic record
Abstract
Nuclear accumulation of the polyglutamine-expanded mutant huntingtin protein remains one of the most predictive cell biological phenotypes of Huntington's disease (HD) progression in patient brain samples and mouse models of the disease. Yet, the relationship between huntingtin nuclear import, neuronal dysfunction and toxicity is not fully understood and it remains unclear whether nuclear accumulation is required for disease onset. Here, we discuss several studies that have guided current understanding of this subject, and highlight our recent data detailing the discovery of a karyopherin β1/β2-type nuclear localization signal near the N-terminus of huntingtin. This signal can function through multiple pathways of nuclear import, and may also be responsible for huntingtin import into the primary cilium. This work represents a significant step forward in our knowledge of the regulatory pathways that govern huntingtin nuclear accumulation and will allow direct examination of both normal and mutant huntingtin nuclear function. This work also suggests a re-examination of the cell biology of any protein that contains a multi-pathway nuclear localization signal. The possibility of targeting huntingtin nuclear import therapeutically and the potential impacts of such a strategy for the treatment of HD are also discussed.
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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".