Autoantibodies to Epilepsy-Related LGI1 in Limbic Encephalitis Neutralize LGI1-ADAM22 Interaction and Reduce Synaptic AMPA Receptors
Bibliographic record
Abstract
More than 30 mutations in LGI1, a secreted neuronal protein, have been reported with autosomal dominant lateral temporal lobe epilepsy (ADLTE). Although LGI1 haploinsufficiency is thought to cause ADLTE, the underlying molecular mechanism that results in abnormal brain excitability remains mysterious. Here, we focused on a mode of action of LGI1 autoantibodies associated with limbic encephalitis (LE), which is one of acquired epileptic disorders characterized by subacute onset of amnesia and seizures. We comprehensively screened human sera from patients with immune-mediated neurological disorders for LGI1 autoantibodies, which also uncovered novel autoantibodies against six cell surface antigens including DCC, DPP10, and ADAM23. Our developed ELISA arrays revealed a specific role for LGI1 antibodies in LE and concomitant involvement of multiple antibodies, including LGI1 antibodies in neuromyotonia, a peripheral nerve disorder. LGI1 antibodies associated with LE specifically inhibited the ligand-receptor interaction between LGI1 and ADAM22/23 by targeting the EPTP repeat domain of LGI1 and reversibly reduced synaptic AMPA receptor clusters in rat hippocampal neurons. Furthermore, we found that disruption of LGI1-ADAM22 interaction by soluble extracellular domain of ADAM22 was sufficient to reduce synaptic AMPA receptors in rat hippocampal neurons and that levels of AMPA receptor were greatly reduced in the hippocampal dentate gyrus in the epileptic LGI1 knock-out mouse. Therefore, either genetic or acquired loss of the LGI1-ADAM22 interaction reduces the AMPA receptor function, causing epileptic disorders. These results suggest that by finely regulating the synaptic AMPA receptors, the LGI1-ADAM22 interaction maintains physiological brain excitability throughout life.
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 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) | 0.000 | 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".