Antibodies to the RNA binding protein heterogeneous nuclear ribonucleoprotein A1 contribute to neuronal cell loss in an animal model of multiple sclerosis
Bibliographic record
Abstract
Abstract Neurodegeneration, including loss of neurons and axons, is a feature of progressive forms of multiple sclerosis (MS). The mechanisms underlying neurodegeneration are mostly unknown. Research implicates autoimmunity to nonmyelin self‐antigens as important contributors to disease pathogenesis. Data from our lab implicate autoimmunity to the RNA binding protein (RBP) heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) as a possible mechanism of neurodegeneration in MS. MS patients make antibodies to hnRNP A1, which have been shown to lead to neuronal dysfunction in vitro. Using an animal model of MS, experimental autoimmune encephalomyelitis (EAE), we show here that injection of anti‐hnRNP A1 antibodies, in contrast to control antibodies, resulted in worsened disease and increased neurodegeneration. We found a reduction of NeuN + neuronal cell bodies in areas of the ventral gray matter of the spinal cord where anti‐hnRNP A1 antibodies localized. Neurons displayed increased levels of hnRNP A1 nucleocytoplasmic mislocalization and stress granule formation, both markers of neuronal injury. Anti‐hnRNP A1 antibodies were found to surround neuronal cell bodies and interact with CD68 + immune cells via Fc receptors. Additionally, anti‐hnRNP A1 antibodies were found within neuronal cell bodies including those of the ventral spinocerebellar tract (VSCT), a tract previously shown to undergo neurodegeneration in anti‐hnRNP A1 antibody injected EAE mice. Finally, both immune cells and neurons showed increased levels of inducible nitric oxide synthase, another indicator of cell damage. These findings suggest that autoimmunity to RBPs, such as hnRNP A1, play a role in neurodegeneration in EAE with important implications for the pathogenesis of 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".