Syk Is Downstream of Intercellular Adhesion Molecule-1 and Mediates Human Rhinovirus Activation of p38 MAPK in Airway Epithelial Cells
Bibliographic record
Abstract
The airway epithelium is the primary target of inhaled pathogens such as human rhinovirus (HRV). Airway epithelial cells express ICAM-1, the major receptor for HRV. HRV binding to ICAM-1 mediates not only viral entry and replication but also a signaling cascade that leads to enhanced inflammatory mediator production. The specific signaling molecules and pathways activated by HRV-ICAM-1 interactions are not well characterized, although studies in human airway epithelia implicate a role for the p38 MAPK in HRV-induced cytokine production. In the current study, we report that Syk, an important immunoregulatory protein tyrosine kinase, is highly expressed by primary and cultured human airway epithelial cells and is activated in response to infection with HRV16. Biochemical studies revealed that ICAM-1 engagement by HRV and cross-linking Abs enhanced the coassociation of Syk with ICAM-1 and ezrin, a cytoskeletal linker protein. In polarized airway epithelial cells, Syk is diffusely distributed in the cytosol under basal conditions but, following engagement of ICAM-1 by cross-linking Abs, is recruited to the plasma membrane. The enhanced Syk-ICAM-1 association following HRV exposure is accompanied by Syk phosphorylation. ICAM-1 engagement by HRV and cross-linking Abs also induced phosphorylation of p38 in a Syk-dependent manner, and conversely, knockdown of Syk by short interfering (si)RNA substantially diminished p38 activation and IL-8 gene expression. Taken together, these observations identify Syk as an important mediator of the airway epithelial cell inflammatory response by modulating p38 phosphorylation and IL-8 gene expression following ICAM-1 engagement by HRV.
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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) | 0.002 | 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".