Alpha-actinin binding kinetics modulate cellular dynamics and force generation
Bibliographic record
Abstract
The actin cytoskeleton is a key element of cell structure and movement whose properties are determined by a host of accessory proteins. Actin cross-linking proteins create a connected network from individual actin filaments, and though the mechanical effects of cross-linker binding affinity on actin networks have been investigated in reconstituted systems, their impact on cellular forces is unknown. Here we show that the binding affinity of the actin cross-linker α-actinin 4 (ACTN4) in cells modulates cytoplasmic mobility, cellular movement, and traction forces. Using fluorescence recovery after photobleaching, we show that an ACTN4 mutation that causes human kidney disease roughly triples the wild-type binding affinity of ACTN4 to F-actin in cells, increasing the dissociation time from 29 ± 13 to 86 ± 29 s. This increased affinity creates a less dynamic cytoplasm, as demonstrated by reduced intracellular microsphere movement, and an approximate halving of cell speed. Surprisingly, these less motile cells generate larger forces. Using traction force microscopy, we show that increased binding affinity of ACTN4 increases the average contractile stress (from 1.8 ± 0.7 to 4.7 ± 0.5 kPa), and the average strain energy (0.4 ± 0.2 to 2.1 ± 0.4 pJ). We speculate that these changes may be explained by an increased solid-like nature of the cytoskeleton, where myosin activity is more partitioned into tension and less is dissipated through filament sliding. These findings demonstrate the impact of cross-linker point mutations on cell dynamics and forces, and suggest mechanisms by which such physical defects lead to human disease.
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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".