Do cardiac actin mutations lead to altered actomyosin interactions?
Bibliographic record
Abstract
It is currently hypothesized that increased heart muscle contractility leads to hypertrophic cardiomyopathy (HCM), and reduced contractility leads to dilated cardiomyopathy (DCM). To determine if changes in the core interaction between actin and myosin occur due to mutations in the cardiac actin gene (ACTC), we measured the interactions between myosin and 8 ACTC mutant proteins found in patients with HCM or DCM. R312H showed a decreased actin-activated myosin S1 ATPase rate (13.1 ± 0.63 μmol/L/min) compared to WT (15.3 ± 1.6 μmol/L/min), whereas the rate with E99K was significantly higher (20.1 ± 1.5 μmol/L/min). In vitro motility assays with varying ATP concentrations showed that the KM for E99K remains unchanged with a significantly decreased Vmax (1.90 ± 0.37 μm/sec) compared to WT (3.33 ± 0.46 μm/sec). Based on a 5 nm myosin step size, we calculated a duty ratio of approximately 0.04 for WT and the majority of mutant actins; however, the duty ratio for E99K was twice as high. Based on our analysis of 8 ACTC mutants, we infer that mutations in ACTC lead to disease through various molecular mechanisms. While changes in actomyosin interactions with the E99K mutation might cause increased ATP usage and tension leading to HCM, measurable changes in the basic interaction between actin and myosin do not appear to be involved in the mechanisms of disease development for the other ACTC mutants tested.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".