Keratin contribution to cellular mechanical stress response at focal adhesions as assayed by laser tweezers
Bibliographic record
Abstract
The ability of adherent cells to sense and adapt to a mechanical stress generated at focal adhesions (FAs) largely occurs through the integrin-mediated interaction between the cytoskeleton, namely actin microfilaments, and extracellular matrix elements, like fibronectin. Here we assessed the contribution of keratin 8 and 18 (K8/K18) intermediate filaments (IFs) in simple epithelial cells in response to a mechanical stress applied on integrins at FAs. To this end, we used monolayer cultures of K8-knockdown H4-II-E-C3 (shK8b1) rat hepatoma cells and their K8/K18-containing counterparts (H4ev). The stress was generated with a laser tweezers mediated force applied on a fibronectin-coated polystyrene bead attached to integrins alpha5/beta1 forming FAs. Measurement of the bead displacement allowed assessment of the viscoelastic response at FAs and the associated surface membrane stiffness. Notably, the loss of K8/K18 IFs in shK8b1 cells revealed an immediate reduction in bead displacements characteristic of a sudden increased in the FA elastic stiffness, incompatible with the K8/K18 IF intrinsic viscoelastic features, but in line with an induced perturbation of the mechanotransduction signals triggered at integrins. In addition, actin microfilament disruption, and to a lesser extent microtubule disruption, led to prominent decreases in the elastic stiffness of FAs, thus identifying actin-MFs and MTs as modulators of the time-dependent FA stiffening in both H4ev cells and shK8b1 cells, in response to mechanical stress. On technical ground, the laser tweezers offer a tool of choice to delineate the K8/K18 IF-mediated modulation of cytoskeletal versus signaling activities at FAs in epithelial cells in response to mechanical stress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".