Autoimmunity to a ribonucleoprotein drives neuron loss in multiple sclerosis models
Bibliographic record
Abstract
Neurodegeneration, the progressive loss or damage to neurons and axons, underlies permanent disability in multiple sclerosis (MS); yet its mechanisms are incompletely understood. Recent data indicates autoimmunity to several intraneuronal antigens, including the RNA binding protein (RBP) heterogenous nuclear ribonucleoprotein A1 (hnRNP A1), as contributors to neurodegeneration. We previously showed that addition of anti-hnRNP A1 antibodies, which target the same immunodominant domain of MS IgG, to mice with experimental autoimmune encephalomyelitis (EAE) worsened disease and resulted in an exacerbation of hnRNP A1 dysfunction including cytoplasmic mislocalization of hnRNP A1, stress granule (SG) formation and neurodegeneration in the chronic stages of disease. Because this previous study focused on a singular timepoint during EAE, it is unclear whether anti-hnRNP A1 antibody induced hnRNP A1 dysfunction caused neurodegeneration or was result of it. In the present study, we analyzed in vivo and in vitro models of anti-hnRNP A1 antibody-mediated autoimmunity for markers of hnRNP A1 dysfunction and neurodegeneration over a time course to gain a better understanding of the connection between hnRNP A1 dysfunction and neurodegeneration. Anti-hnRNP A1 antibody treatment resulted in increased neuronal hnRNP A1 mislocalization and nuclear depletion temporally followed by altered RNA expression and SG formation, and lastly an increase in necroptotic signalling and neuronal cell death. Treatment with necrostatin-1s inhibited necroptosis and partially rescued anti-hnRNP A1 antibody-mediated neurodegeneration while clathrin knockdown specifically inhibited anti-hnRNP A1 antibody uptake into neurons. This data identifies a novel antibody-mediated mechanism of neurodegeneration, which may be targeted to inhibit neurodegeneration and prevent permanent neurological decline in persons living with MS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".