Huntingtin is an RNA-binding protein and participates in NEAT1-mediated paraspeckles
Bibliographic record
Abstract
Abstract Huntingtin protein, mutated in Huntington disease, is implicated in nucleic acid- mediated processes, yet evidence for direct huntingtin-nucleic acid interaction is limited. Here we show wildtype and mutant huntingtin co-purify with nucleic acids, primarily RNA, and interact directly with G-rich RNAs in in vitro assays. Huntingtin RNA immunoprecipitation sequencing from patient-derived fibroblasts and neuronal progenitor cells expressing wildtype and mutant huntingtin revealed NEAT1 as a significantly enriched transcript. Altered NEAT1 levels were evident in Huntington’s disease cells and postmortem brain tissues, and huntingtin knockdown decreased NEAT1 levels. Huntingtin co-localized with NEAT1 in paraspeckles, and we identified a high-affinity RNA motif preferred by huntingtin. This study highlights NEAT1 as a novel huntingtin interactor, demonstrating huntingtin’s involvement in RNA-mediated functions and paraspeckle regulation. One-Sentence Summary HTT is an RNA-binding protein that interacts with G-rich sequences, including those in the paraspeckle lncRNA NEAT1.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".