Cdc42 regulates cytokine expression and trafficking in bronchial epithelial cells
Bibliographic record
Abstract
ABSTRACT Airway epithelial cells can respond to incoming pathogens, allergens and stimulants through the secretion of cytokines and chemokines. These pro-inflammatory mediators activate inflammatory signaling cascades that allow a robust immune response to be mounted. However, uncontrolled production and release of cytokines and chemokines can result in chronic inflammation and appears to be an underlying mechanism for the pathogenesis of pulmonary disorders such as asthma and COPD. The Rho GTPase, Cdc42, is an important signaling molecule that we hypothesize can regulate cytokine production and release from epithelial cells. We treated BEAS-2B lung epithelial cells with a set of stimulants to activate inflammatory pathways and cytokine release. The production, trafficking and secretion of cytokines were assessed when Cdc42 was pharmacologically inhibited with ML141 drug or silenced with lentiviral-mediated shRNA knockdown. We found that Cdc42 inhibition with ML141 differentially affected gene expression of a subset of cytokines; transcription of IL-6 and IL-8 were increased while MCP-1 was decreased. However, Cdc42 inhibition or depletion disrupted IL-8 trafficking and reduced its secretion even though transcription was increased. Cytokines transiting through the Golgi were particularly affected by Cdc42 disruption. Our results define a role for Cdc42 in the regulation of cytokine production and release in airway epithelial cells. This underscores the role of Cdc42 in coupling receptor activation to downstream gene expression and also as a regulator of cytokine secretory pathways. Short Summary Cytokine secretion from airway epithelial cells contributes to the pathogenesis of disease. We show that Cdc42 regulates cytokine gene expression and is required for cytokine secretion via control of transport through the Golgi complex.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".