α4β1 Integrin/VCAM-1 Interaction Activates αLβ2 Integrin-Mediated Adhesion to ICAM-1 in Human T Cells
Bibliographic record
Abstract
Modulation of integrin affinity and/or avidity provides a regulatory mechanism by which leukocyte adhesion to endothelium is strengthened or weakened at different stages of emigration. In this study, we demonstrate that binding of high-affinity alpha 4 beta 1 integrins to VCAM-1 strengthens alpha L beta 2 integrin-mediated adhesion. The strength of adhesion of Jurkat cells, a human leukemia T cell line, or MnCl2-treated peripheral blood T cells to immobilized chimeric human VCAM-1/Fc, ICAM-1/Fc, or both was quantified using parallel plate flow chamber leukocyte detachment assays in which shear stress was increased incrementally (0.5-30 dynes/cm2). The strength of adhesion to VCAM-1 plus ICAM-1, or to a 40-kDa fragment of fibronectin containing the CS-1 exon plus ICAM-1, was greater than the sum of adhesion to each molecule alone. Treatment of Jurkat or blood T cells with soluble cross-linked VCAM-1/Fc or HP2/1, a mAb to alpha 4, significantly increased adhesion to ICAM-1. These treatments induced clustering of alpha L beta 2 integrins, but not the high-affinity beta 2 integrin epitope recognized by mAb 24. Up-regulated adhesion to ICAM-1 was abolished by cytochalasin D, an inhibitor of cytoskeletal rearrangement. Taken together, our data suggest that the binding of VCAM-1 or fibronectin to alpha 4 beta 1 integrins initiates a signaling pathway that increases beta 2 integrin avidity but not affinity. A role for the cytoskeleton is implicated in this process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".