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 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".