Dissecting focal adhesions in cells differentially expressing calreticulin: a microscopy study
Bibliographic record
Abstract
BACKGROUND INFORMATION: Our previous studies have shown that calreticulin, a Ca2+-binding chaperone located in the endoplasmic reticulum, affects cell-substratum adhesions via the induction of vinculin and N-cadherin. Cells overexpressing calreticulin contain more vinculin than low expressers and make abundant contacts with the substratum. However, cells that express low levels of calreticulin exhibit a weak adhesive phenotype and make few, if any, focal adhesions. To date, the identity of the types of focal adhesions made by calreticulin overexpressing and low expressing cells has not been dissected. RESULTS: The results of the present study show that calreticulin affects fibronectin matrix assembly in L fibroblast cell lines that differentially express the protein, and that these cells also differ profoundly in focal adhesion formation. Although the calreticulin overexpressing cells generate numerous interference-reflection-microscopy-dark, vinculin- and paxillin-containing classical focal contacts, as well as some fibrillar adhesions, the cells expressing low levels of calreticulin generate only a few weak focal adhesions. The fibronectin receptor was found to be clustered in calreticulin overexpressing cells, but diffusely distributed over the cell surface in low expressing cells. Plating L fibroblasts on fibronectin-coated substrata induced extensive spreading in all cell lines tested. However, although calreticulin overexpressing cells were induced to form classical vinculin-rich focal contacts, the low calreticulin expressing cells overcame their weak adhesive phenotype by induction of many tensin-rich fibrillar adhesions, thus compensating for the low level of vinculin in these cells. CONCLUSIONS: We propose that calreticulin affects fibronectin production and, thereby, assembly, and it indirectly influences the formation and/or stability of focal contacts and fibrillar adhesions, both of which are instrumental in matrix assembly and remodelling.
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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 | 0.001 |
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".