Fer Kinase Limits Neutrophil Chemotaxis toward End Target Chemoattractants
Bibliographic record
Abstract
Neutrophil recruitment and directional movement toward chemotactic stimuli are important processes in innate immune responses. This study examines the role of Fer kinase in neutrophil recruitment and chemotaxis to various chemoattractants in vitro and in vivo. Mice targeted with a kinase-inactivating mutation (Fer(DR/DR)) or wild type (WT) were studied using time-lapse intravital microscopy to examine leukocyte recruitment and chemotaxis in vivo. In response to keratinocyte-derived cytokine, no difference in leukocyte chemotaxis was observed between WT and Fer(DR/DR) mice. However, in response to the chemotactic peptide WKYMVm, a selective agonist of the formyl peptide receptor, a 2-fold increase in leukocyte emigration was noted in Fer(DR/DR) mice (p < 0.05). To determine whether these defects were due to Fer signaling in the endothelium or other nonhematopoietic cells, bone marrow chimeras were generated. WKYMVm-induced leukocyte recruitment in chimeric mice (WT bone marrow to Fer(DR/DR) recipients or vice versa) was similar to WT mice, suggesting that Fer kinase signaling in both leukocytes and endothelial cells serves to limit chemotaxis. Purified Fer(DR/DR) neutrophils demonstrated enhanced chemotaxis toward end target chemoattractants (WKYMVm and C5a) compared with WT using an under-agarose gel chemotaxis assay. These defects were not observed in response to intermediate chemoattractants (keratinocyte-derived cytokine, MIP-2, or LTB(4)). Increased WKYMVm-induced chemotaxis of Fer(DR/DR) neutrophils correlated with sustained PI3K activity and reduced reliance on the p38 MAPK pathway compared with WT neutrophils. Together, these data identify Fer as a novel inhibitory kinase for neutrophil chemotaxis toward end target chemoattractants through modulation of PI3K activity.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".