Twisting integrin receptors increases endothelin-1 gene expression in endothelial cells
Bibliographic record
Abstract
A magnetic twisting stimulator was developed based on the previously published technique of magnetic twisting cytometry. Using ligand-coated ferromagnetic microbeads, this device can apply mechanical stresses with varying amplitudes, duration, frequencies, and waveforms to specific cell surface receptors. Biochemical and biological responses of the cells to the mechanical stimulation can be assayed. Twisting integrin receptors with RGD (Arg-Gly-Asp)-containing peptide-coated beads increased endothelin-1 (ET-1) gene expression by >100%. In contrast, twisting scavenger receptors with acetylated low-density lipoprotein-coated beads or twisting HLA antigen with anti-HLA antibody-coated beads did not lead to alterations in ET-1 gene expression. In situ hybridization showed that the increase in ET-1 mRNA was localized in the cells that were stressed with the RGD-coated beads. Blocking stretch-activated ion channels with gadolinium, chelating Ca2+ with EGTA, or inhibiting tyrosine phosphorylation with genistein abolished twist-induced ET-1 mRNA elevation. Abolishing cytoskeletal tension with an inhibitor of the myosin ATPase, with an inhibitor of myosin light chain kinase, or with an actin microfilament disrupter blocked twisted-induced increases in ET-1 expression. Our results are consistent with the hypothesis that the molecular structural linkage of integrin-cytoskeleton is an important pathway for stress-induced ET-1 gene expression.
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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".