hnRNP A1 dysfunction alters RNA splicing and drives neurodegeneration in multiple sclerosis (MS)
Bibliographic record
Abstract
Neurodegeneration is the primary driver of disease progression in multiple sclerosis (MS) resulting in permanent disability, creating an urgent need to discover its underlying mechanisms. Herein, we establish that dysfunction of the RNA binding protein heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) results in differential of binding to RNA targets causing alternative RNA splicing, which contributes to neurodegeneration in MS and its models. Using RNAseq of MS brains, we discovered differential expression and aberrant splicing of hnRNP A1 target RNAs involved in neuronal function and RNA homeostasis. We confirmed this in vivo in experimental autoimmune encephalomyelitis employing CLIPseq specific for hnRNP A1, where hnRNP A1 differentially binds and regulates RNA, including aberrantly spliced targets identified in human samples. Additionally, dysfunctional hnRNP A1 expression in neurons caused neurite loss and identical changes in splicing, corroborating hnRNP A1 dysfunction as a cause of neurodegeneration. Collectively, these data indicate hnRNP A1 dysfunction causes altered neuronal RNA splicing, resulting in neurodegeneration 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".