Distinct pathogenic influence of anti-HMGCR+ and anti-SRP+ immune-mediated necrotizing myopathy autoantibodies on engineered muscle function
Bibliographic record
Abstract
Abstract Immune-mediated necrotizing myopathy (IMNM) is a subgroup of idiopathic inflammatory myopathies associated with anti-signal recognition particle (SRP) or anti-3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) autoantibodies. However, the demonstration of a direct pathogenic effect of IMNM patient autoantibodies on skeletal muscle contractile force, independent of the downstream activation of the complement pathway, has yet to be reported. Thus, the goal of this study was to leverage a custom 3D-human skeletal muscle microtissue (hMMT) culture platform, that enables muscle cell contractile apparatus maturation and the analysis of contractile function, to evaluate the direct effect of total immunoglobulins (IgGs) isolated from IMNM patients with amplification of anti-SRP + or anti-HMGCR + autoantibodies. hMMTs capable of force generation were treated with total IgGs, isolated from 3 SRP+ and 3 HMGCR+ patients plasma, and delivered in complement inactivated media for 4 days. hMMT health was then evaluated by quantifying the peak force and contraction kinetics in response to electrical field stimulation and by performing histological analysis of sarcomere and myotube structures. Treating hMMTs with total IgGs from anti-HMGCR + patients resulted in a decline in tetanus contractile force, though sarcomere Z-line architecture analysis revealed no significant influences on sarcomere organization. hMMT treatment with total IgGs from anti-SRP + patients induced muscle atrophy, observed via significantly smaller myotube diameter, but this did not translate to a decline in contractile function. This study demonstrates that anti-SRP and anti-HMGCR autoantibodies exert direct, but distinct influences on IMNM-associated skeletal muscle pathogenesis, which may inform IMNM therapy development.
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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 | 0.000 |
| 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".