Gene-specific response to MuSK agonist antibody in the treatment of Congenital Myasthenic Syndromes
Bibliographic record
Abstract
Abstract Congenital myasthenic syndromes (CMS) are a group of rare disorders characterized by fatigable muscle weakness and caused by impaired neuromuscular junction (NMJ) function. CMS symptoms are highly variable, but can be detrimental and lead to death. There are over 40 different genetic subtypes, including Agrn- CMS and ColQ- CMS. Agrn encodes for neural AGRIN, which is released from the nerve terminal and triggers muscle-specific kinase phosphorylation (pMuSK). pMuSK is essential for NMJ development and maintenance, thus AGRIN deficiency causes NMJ impairment. ColQ encodes for collagenous subunit Q (ColQ), which anchors acetylcholinesterase and stabilizes MuSK. As a result, ColQ deficiency results in NMJ degeneration from prolonged transmission signals and decreased pMuSK. Current treatments for Agrn- CMS and ColQ- CMS are limited, highlighting the importance of finding more efficient therapies. Recently, a MuSK agonist antibody with high affinity for the Frizzled-like domain showed remarkable rescue of a Dok7 -CMS mouse model. We hypothesized a similar antibody could benefit Agrn- and ColQ- CMS mouse models. Agrn- CMS mice were treated at postnatal day 5 (P5), P15 and P35, and ColQ- CMS mice were treated weekly from P22 to P57. In Agrn- CMS mice, 3B2 treatment rescued survival, bodyweight, fibre type switching and pMuSK levels, and improved grip strength and NMJ morphology. In ColQ- CMS mice, 3B2 treatment was unable to rescue deficits observed. Our findings suggest that MuSK agonists may benefit patients with Agrn -CMS, which should be tested in clinical trials. Our study emphasizes that effective CMS treatment is gene-dependent and relies on an accurate genetic diagnosis.
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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) | 0.000 | 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 | 0.001 |
| 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".