Cytohesin-1 regulates fMLF-mediated activation and functions of the β2 integrin Mac-1 in human neutrophils
Bibliographic record
Abstract
The nucleotide exchange factor cytohesin-1 was previously reported to interact with the cytoplasmic domains of the integrin β-chain common to all β(2) integrins such as LFA-1 and Mac-1. We show here that cytohesin-1, which contributes to fMLF-induced functional responses in PMNs through activation of Arf6, restrains the activation of the β(2) integrin Mac-1 (αMβ(2)) in PMNs or dcAMP-differentiated PLB-985 cells. We found that the cytohesin-1 inhibitor SecinH3 or siRNA increased cell adhesion to immobilized fibrinogen and fMLF-mediated conformational changes of Mac-1, monitored using mAb CBRM1/5, specific for the activation epitope of the αM subunit. In contrast, PLB-985 cells overexpressing cytohesin-1 showed little adhesion to fibrinogen. The use of SecinH3 and siRNA also revealed that interference with cytohesin-1 signaling also enhanced phagocytosis of zymosan particles and chemotaxis toward fMLF in transwell migration assays. These increments of phagocytosis and chemotaxis in cells treated with SecinH3 and cytohesin-1 siRNA were reversed by a blocking mAb to the integrin-αM subunit. We provide evidence for increased polymerized cortical actin in cells treated with SecinH3 and that altered signaling through cytohesin-1 increased cell surface expression of FPRL-1 and impairs the late calcium mobilization response elicited by fMLF. The data provide evidence that stimulation with fMLF initiates a signaling cascade that restrains Mac-1 activation in PMNs. Such crosstalk between FPRL-1 and Mac-1 involves cytohesin-1. We suggest that cytohesin-1 may coordinate activation of the β(2) integrins to regulate PMN adhesion, phagocytosis, and chemotaxis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".