Multiple Sclerosis-Associated hnRNPA1 Mutations Alter hnRNPA1 Dynamics and Influence Stress Granule Formation
Bibliographic record
Abstract
Evidence indicates that dysfunctional heterogeneous ribonucleoprotein A1 (hnRNPA1; A1) contributes to the pathogenesis of neurodegeneration in multiple sclerosis. Understanding molecular mechanisms of neurodegeneration in multiple sclerosis may result in novel therapies that attenuate neurodegeneration, thereby improving the lives of MS patients with multiple sclerosis. Using an in vitro, blue light induced, optogenetic protein expression system containing the optogene Cryptochrome 2 and a fluorescent mCherry reporter, we examined the effects of multiple sclerosis-associated somatic A1 mutations (P275S and F281L) in A1 localization, cluster kinetics and stress granule formation in real-time. We show that A1 mutations caused cytoplasmic mislocalization, and significantly altered the kinetics of A1 cluster formation/dissociation, and the quantity and size of clusters. A1 mutations also caused stress granule formation to occur more quickly and frequently in response to blue light stimulation. This study establishes a live cell optogenetics imaging system to probe localization and association characteristics of A1. It also demonstrates that somatic mutations in A1 alter its function and promote stress granule formation, which supports the hypothesis that A1 dysfunction may exacerbate neurodegeneration in multiple sclerosis.
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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".