Regulation of Tension-induced Mechanotranscriptional Signals by the Microtubule Network in Fibroblasts
Bibliographic record
Abstract
Mechanical loading of connective tissues induces the expression of extracellular matrix and cytoskeletal genes that are involved in matrix remodeling. These processes depend in part on force transmission through beta1 integrins and actin filaments, but the role of microtubules in regulating mechanotranscriptional responses is not well defined. We assessed the involvement of microtubules in the mechanotranscriptional regulation of filamin A, an actin-cross-linking protein that protects cells against force-induced apoptosis by stabilizing cell membranes. Collagen-coated magnetite beads and magnetic fields were used to apply tensile forces to cultured fibroblasts at focal adhesions. Force enhanced recruitment of alpha-tubulin and the plus end microtubule-binding protein cytoplasmic linker protein-170 (CLIP-170) at focal adhesions. Immunoprecipitation studies demonstrated no direct binding of tubulin to actin or filamin A, but CLIP-170 interacted with tubulin, filamin A, and beta-actin. The association of CLIP-170 with beta-actin was enhanced by force. Force activated the p38 mitogen-activated protein kinase, increased filamin A expression, and induced the relocation of p38 and filamin A to focal adhesions. Disruption of microtubules with nocodazole, independent of force application, enhanced filamin A expression and Sp1-mediated filamin A promoter activity, while stabilization of microtubules with Taxol inhibited force induction of both filamin A mRNA and protein. We conclude that in response to tensile forces applied through beta1 integrins and actin the microtubule network modulates mechanotranscriptional coupling of filamin A.
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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 | 0.000 | 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".