Anti-inflammatory effects of zinc and alterations in zinc transporter mRNA in mouse models of allergic inflammation
Bibliographic record
Abstract
There is clinical evidence linking asthma with the trace element, zinc (Zn). Using a mouse model of allergic inflammation, we have previously shown that labile Zn decreases in inflamed airway epithelium (Truong-Tran AQ, Ruffin RE, Foster PS, Koskinen AM, Coyle P, Philcox JC, Rofe AM, Zalewski PD. Am J Respir Cell Mol Biol 27: 286-296, 2002). Moreover, mild nutritional Zn deficiency worsens lung function. Recently, a number of proteins belonging to the Solute Carrier Family 39 (ZIP) and Solute Carrier Family 30 (ZnT) have been identified that bind Zn and regulate Zn homeostasis. Mice were sensitized, and subsequently aerochallenged, with ovalbumin to induce acute and chronic airway inflammation. Mice received 0, 54, or 100 microg of Zn intraperitoneally. Tissues were analyzed for Zn content and histopathology. Inflammatory cells were counted in bronchoalveolar lavage fluid. Cytokine and Zn transporter mRNA levels were determined by cDNA gene array and/or real-time PCR. Zn supplementation decreased bronchoalveolar lavage fluid eosinophils by 40 and 80%, and lymphocytes by 55 and 66%, in the acute and chronic models, respectively. Alterations in Zn transporter expression were observed during acute inflammation, including increases in ZIP1 and ZIP14 and decreases in ZIP4 and ZnT4. Zn supplementation normalized ZIP1 and ZIP14, but it did not affect mRNA levels of cytokines or their receptors. Our results indicate that inflammation-induced alterations in Zn transporter gene expression are directed toward increasing Zn uptake. Increases in Zn uptake may be needed to counteract the local loss of Zn in the airway and to meet an increased demand for Zn-dependent proteins. The reduction of inflammatory cells by Zn in the airways provides support for Zn supplementation trials in human asthmatic individuals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".