β<sub>1</sub> integrins are distributed in adhesion structures with fibronectin and caveolin and in coated pits
Bibliographic record
Abstract
Integrins are found in adhesion structures, which link the extracellular matrix to cytoskeletal proteins. Here, we attempt to further define the distribution of beta1 integrins in the context of their association with matrix proteins and other cell surface molecules relevant to the endocytic process. We find that beta1 integrins colocalize with fibronectin in fibrillar adhesion structures. A fraction of caveolin is also organized along these adhesion structures. The extracellular matrix protein laminin is not concentrated in these structures. The alpha4beta1 integrin exhibits a distinct distribution from other beta1 integrins after cells have adhered for 1 h to extracellular matrix proteins but is localized in adhesion structures after 24 h of adhesion. There are differences between the fibronectin receptors: alpha5beta1 integrins colocalize with adaptor protein-2 in coated pits, while alpha4beta1 integrins do not. This parallels our earlier observation that of the two laminin receptors, alpha1beta1 and alpha6beta1, only alpha1beta1 integrins colocalize with adaptor protein-2 in coated pits. Calcium chelation or inhibition of mitogen-activated protein kinase kinase, protein kinase C, or src did not affect localization of alpha1beta1 and alpha5beta1 integrins in coated pits. Likewise, the integrity of coated-pit structures or adhesion structures is not required for integrin and adaptor protein-2 colocalization. This suggests a robust and possibly constitutive interaction between these integrins and coated pits.
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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 | 0.001 | 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".