Molecular Control of Non-Muscle Myosin II-A Aggregation and Intracellular Dynamics by motor- or tail-specific <i>MYH9</i> Mutations
Bibliographic record
Abstract
-RD). While correlation between genotype and phenotype is not well characterized at a molecular level, motor mutations seem to cause more severe phenotypes than tail mutations. Motor domain mutation N93K, previously described as activity-impairing, causes in fact an almost non-significant defect on motor function in vitro. Conversely, it increases NM2-A filament stability and interaction with the myosin chaperone UNC45a in stress fiber-forming cells. This also alters its subcellular localization and effect on adhesion dynamics. Similar cellular effects are observed in cells expressing NM2-A E1841K, a prototypical tail mutation. In cells devoid of stress fibers such as megakaryocytes, NM2-A N93K forms large, amorphous, concentration-dependent aggregates that also contain wild type NM2-A and UNC45a. Conversely, NM2-A E1841K forms concentration-independent aggregates that exclude wild type NM2-A and UNC45a. Our data shows that the N93K mutation reduces the fraction of functional cellular NM2-A by enhancing the stability of NM2-A filaments and/or promoting protein aggregation together with wild type NM2-A. Conversely, NM2-A E1841K form aggregates that do not affect wild type NM2-A. These observations are consistent with the molecular severity observed in primary cells from patients of these genotypes.
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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) | 0.002 | 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".