Dysfunctional RNA binding protein induced neurodegeneration is attenuated by inhibition of the integrated stress response
Bibliographic record
Abstract
Dysfunction of the RNA binding protein heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) contributes to neurodegeneration, the primary cause of permanent disability in multiple sclerosis (MS). To better understand the role of hnRNP A1 dysfunction in the pathogenesis of neurodegeneration, we utilized optogenetics-driven hnRNP A1 clustering to model its dysfunction in neuron-like differentiated Neuro-2A cells. hnRNP A1 clustering activates the integrated stress response (ISR) and results in a neurodegenerative phenotype marked by decreased neuronal protein translation and neurite loss. Small molecule inhibition of the ISR with either PERKi (GSK2606414) or ISRIB (integrated stress response inhibitor) attenuated both the decrease in neuronal translation and neurite loss, without affecting hnRNP A1 clustering. We then confirmed a strong association between hnRNP A1 clustering and ISR activation in neurons from MS brains. These data illustrate that hnRNP A1 dysfunction promotes neurodegeneration by activation of the ISR in vitro and in vivo, thus revealing a novel therapeutic target to reduce neurodegeneration and subsequent disability in MS.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".