Autoimmune RNA dysregulation and seizures: therapeutic prospects in neuropsychiatric lupus
Bibliographic record
Abstract
Lupus autoimmunity frequently presents with neuropsychiatric manifestations, but underlying etiology remains poorly understood. Human brain cytoplasmic 200 RNA (BC200 RNA) is a translational regulator in neuronal synapto-dendritic domains. Here, we show that a BC200 guanosine-adenosine dendritic transport motif is recognized by autoantibodies from a subset of neuropsychiatric lupus patients. These autoantibodies impact BC200 functionality by quasi irreversibly displacing two RNA transport factors from the guanosine-adenosine transport motif. Such anti-BC autoantibodies, which can gain access to brains of neuropsychiatric lupus patients, give rise to clinical manifestations including seizures. To establish causality, naive mice with a permeabilized blood-brain barrier were injected with anti-BC autoantibodies from lupus patients with seizures. Animals so injected developed seizure susceptibility with high mortality. Seizure activity was entirely precluded when animals were injected with lupus anti-BC autoantibodies together with BC200 decoy autoantigen. Seizures are a common clinical manifestation in neuropsychiatric lupus, and our work identifies anti-BC autoantibody activity as a mechanistic cause. The results demonstrate potential utility of BC200 decoys for autoantibody-specific therapeutic interventions in neuropsychiatric lupus.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".