β1 integrin-extracellular matrix protein interaction modulates the migratory response to chemokine stimulation
Bibliographic record
Abstract
It is well established that chemokines have a major role in the stimulation of cell movement on extracellular matrix (ECM) substrates. However, it is also clear that ECM substrates may influence the ability of cells to undergo migration. Using the migration chamber method, we assessed the migratory response of human embryonic kidney-293 (HEK) transfectant cells expressing the CC chemokine receptor 5 (CCR5) (HEK-CCR5) to stimulation by chemokines (macrophage inflamatory protein (MIP)-1alpha, MIP-1beta, and regulated on activation normal-T cell expressed and secreted (RANTES)) on ECM substrates (collagen type I and fibronectin). Using filters coated with collagen (20 microg/mL), results showed that the chemokines differed in their ability to elicit cell movement according to the order MIP-1beta > RANTES MIP-1alpha. In contrast, using filters coated with fibronectin (20 microg/mL), all three chemokines were similar in their ability to stimulate migration of HEK-CCR5 cells. In addition, the migratory response with respect to the concentrations of ECM substrates appeared biphasic: thus, chemokine-stimulated cell movement was inhibited at high ECM concentrations (100 microg/mL). To determine the involvement of beta1 integrins, results showed that the migratory response to chemokine stimulation on collagen was largely inhibited by monoclonal antibody (mAb) to alpha2beta1; however, complete inhibition required a combination of mAbs to alpha1beta1 and alpha2beta1. In comparison, migration on fibronectin was inhibited by mAb to alpha3beta1 and alpha5beta1. Our results suggest that the migratory response to CCR5 stimulation may vary quantitatively with both the CCR5 ligand (MIP-1alpha, MIP-1beta, and RANTES), as well as the nature and concentration of the ECM substrate involved.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".