Zinc chelators inhibit eotaxin, RANTES, and MCP-1 production in stimulated human airway epithelium and fibroblasts
Bibliographic record
Abstract
Asthma is characterized by an increased production of eosinophil-active C-C chemokines by the airway epithelium. Recent studies have identified the presence of important quantities of labile zinc in the conducting airways. We hypothesized that modulation of this labile zinc could influence the production of proinflammatory chemokines in respiratory epithelial cells. The zinc chelator N,N,N',N'-tetrakis(2-pyridylmethyl)ethylenediamine (TPEN) and the heavy metal chelator 2,3-dimercapto-1-propanesulfonic acid (DMPS) were used to reduce the labile zinc content of A549, BEAS-2B, and HFL-1 cells. Northern blot analysis and RNase protection assay were used to study the effects of the zinc chelators on mRNA expression. DMPS and TPEN specifically inhibited the production of eotaxin, regulated on activation, normal T-cell expressed, and presumably secreted, and monocyte chemotactic protein-1 in TNF-alpha-stimulated respiratory epithelial cells and fibroblasts through labile zinc chelation. The inhibitory effects of DMPS and TPEN were associated with the decreased binding of the zinc-finger transcription factor GATA-1, whereas no change in NF-kappaB activation was observed. Together these results demonstrate that modulation of the labile pool of zinc can regulate gene expression and protein synthesis of C-C chemokines in lung epithelial cells and fibroblasts.
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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 | 0.000 |
| 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".