Arginine methylation of RNA-binding proteins is impaired in Huntington’s disease
Bibliographic record
Abstract
Huntington's disease (HD) is a progressive neurodegenerative disorder caused by a CAG repeat expansion in the HD gene, coding for huntingtin protein (HTT). Mechanisms of HD cellular pathogenesis remain undefined and likely involve disruptions in many cellular processes and functions presumably mediated by abnormal protein interactions of mutant HTT. We previously found HTT interaction with several protein arginine methyl-transferase (PRMT) enzymes. Protein arginine methylation mediated by PRMT enzymes is an important post-translational modification with an emerging role in neurodegeneration. We found that normal (but not mutant) HTT can facilitate the activity of PRMTs in vitro and the formation of arginine methylation complexes. These interactions appear to be disrupted in HD neurons. This suggests an additional functional role for HTT/PRMT interactions, not limited to substrate/enzyme relationship, which may result in global changes in arginine protein methylation in HD. Our quantitative analysis of striatal precursor neuron proteome indicated that arginine protein methylation is significantly altered in HD. We identified a cluster highly enriched in RNA-binding proteins with reduced arginine methylation, which is essential to their function in RNA processing and splicing. We found that several of these proteins interact with HTT, and their RNA-binding and localization are affected in HD cells likely due to a compromised arginine methylation and/or abnormal interactions with mutant HTT. These studies reveal a potential new mechanism for disruption of RNA processing in HD, involving a direct interaction of HTT with methyl-transferase enzymes and modulation of their activity and highlighting methylation of arginine as potential new therapeutic target for HD.
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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".