Unraveling calcium dysregulation and autoimmunity in immune mediated rippling muscle disease
Bibliographic record
Abstract
Rippling Muscle Disease (RMD) is a rare skeletal myopathy characterized by abnormal muscular excitability manifesting with wave-like muscle contractions and percussion-induced muscle mounding. Hereditary RMD is associated with caveolin-3 or cavin-1 mutations. Recently, we identified cavin 4 autoantibodies as a biomarker of immune-mediated RMD (iRMD), though the underlying disease-mechanisms remain poorly understood. Transcriptomic studies were performed on muscle biopsies of 8 patients (5 males; 3 females; ages 26-to-80) with iRMD. Subsequent pathway analysis compared iRMD to human non-disease control and disease control (dermatomyositis) muscle samples. Transcriptomic studies demonstrated changes in key pathways of muscle contraction and development. All iRMD samples had significantly upregulated cavin-4 expression compared to controls, likely compensatory for autoantibody-mediated protein degradation. Proteins involved in muscle relaxation (including SERCA1, PMCA and PLN) were significantly increased in iRMD compared to controls. Comparison of iRMD to dermatomyositis transcriptomics demonstrated significant overlap in immune pathways, and the IL-6 signaling pathway was markedly increased in all iRMD patient muscle biopsies and increased in the majority of iRMD patients' serum. This study represents the first muscle transcriptomic analysis of iRMD patients and dissects underlying disease mechanisms. Increase of sarcolemmal and cellular calcium channels as well as PLN, an inhibitor of the SERCA pump for calcium into the sarcoplasm, likely alters the calcium dynamics in iRMD. These changes in crucial components of muscle relaxation may underlie rippling by altering calcium flux. Our findings provide crucial insights into the differential expression of genes regulating muscle relaxation and highlight potential disease pathomechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".