Epithelium‐derived chemokines induce airway smooth muscle cell migration
Bibliographic record
Abstract
BACKGROUND: The remodelling of airway smooth muscle (ASM) associated with asthma severity may involve the migration of ASM cells towards the epithelium. However, little is known about the mechanisms of cell migration and the effect of epithelial-derived mediators on this process. OBJECTIVE: The main objective of the current study is to assess the effects of epithelial-derived chemokines on ASM cell migration. METHODS: Normal human ASM cells were incubated with supernatants from cells of the bronchial epithelial cell line BEAS-2B and normal human bronchial epithelial (NHBE) cells. To induce chemokine production, epithelial cells were treated with TNF-alpha. Chemokine expression by epithelial cells was evaluated by quantitative real-time PCR, ELISA and membrane antibody array. To identify the role of individual chemokines in ASM cell migration, we performed migration assays with a modified Boyden chamber using specific neutralizing antibodies to block chemokine effects. RESULTS: Supernatants from BEAS-2B cells treated with TNF-alpha increased ASM cell migration; migration was increased 1.6 and 2.5-fold by supernatant from BEAS-2B cells treated with 10 and 100 ng/mL TNF-alpha, respectively. Protein levels in supernatants and mRNA expression by BEAS-2B cells of regulated on activation, normal T cell expressed and secreted (RANTES) and IL-8 were significantly increased by 100 ng/mL TNF-alpha treatment. The incubation of supernatant with antibodies to RANTES or IL-8 significantly reduced ASM cell migration, and the combined antibodies further inhibited the cell migration. The migratory effects of supernatants and inhibiting effects of RANTES and/or IL-8 were confirmed also using NHBE cells. CONCLUSION: The results show that chemokines from airway epithelial cells cause ASM cell migration and might potentially play a role in the process of airway remodelling in asthma.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".