An autoantibody identifies arrhythmogenic right ventricular cardiomyopathy and participates in its pathogenesis
Bibliographic record
Abstract
Aims: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is characterized by right ventricular myocardial replacement and life-threatening ventricular arrhythmias. Desmosomal gene mutations are sometimes identified, but clinical and genetic diagnosis remains challenging. Desmosomal skin disorders can be caused by desmosomal gene mutations or autoantibodies. We sought to determine if anti-desmosome antibodies are present in subjects with ARVC. Methods and results: We evaluated ARVC subjects and controls for antibodies to cardiac desmosomal cadherin proteins. Desmoglein-2 (DSG2), desmocollin-2, and N-cadherin proteins on western blots were exposed to sera, in primary and validation cohorts of subjects and controls, as well as the naturally occurring Boxer dog model of ARVC. We identified anti-DSG2 antibodies in 12/12 and 25/25 definite ARVC cohorts and 7/8 borderline subjects. Antibody was absent in 11/12, faint in 1/12, and absent in 20/20 of two control cohorts. Anti-DSG2 antibodies were present in 10/10 Boxer dogs with ARVC, and absent in 18/18 without. In humans, the level of anti-DSG2 antibodies correlated with the burden of premature ventricular contractions (r = 0.70), and antibodies caused gap junction dysfunction, a common feature of ARVC, in vitro. Anti-DSG2 antibodies were present in ARVC subjects regardless of whether an underlying mutation was identified, or which mutation was present. A disease-specific DSG2 epitope was identified. Conclusion: Anti-DSG2 antibodies are a sensitive and specific biomarker for ARVC. The development of autoimmunity as a result of target-related mutations is unique. Anti-DSG2 antibodies likely explain the cardiac inflammation that is frequently identified in ARVC and may represent a new therapeutic target.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".