Expression of the Th1 Chemokine IFN-γ-Inducible Protein 10 in the Airway Alters Mucosal Allergic Sensitization in Mice
Bibliographic record
Abstract
Although the preliminary characterization of chemokines and their receptors has been prolific, comparatively little is known about the role of chemokines in the evolution of immune responses. We speculate that the preferential recruitment of a particular immune cell population has implications for the short- and long-term features of an adaptive response. To test this hypothesis, we employed adenovirus-mediated gene transfer to express the Th1-affiliated, CXC chemokine IFN-gamma-inducible protein (IP) 10 in the airways of mice undergoing a mucosal sensitization regimen known to result in a Th2-polarized allergic response. This resulted in a approximately 60-75% inhibition of eosinophils in the bronchoalveolar lavage (BAL); these inflammatory changes were accompanied by enhanced IFN-gamma, ablated IL-4, and, peculiarly, unaltered IL-5 and eotaxin levels in the BAL. The effect of IP-10 expression was shown to be dependent on IFN-gamma, as there was no statistically significant reduction in BAL eosinophilia in IFN-gamma knockout mice subjected to the IP-10 intervention. Flow cytometric analysis of mononuclear cells in the lung revealed a approximately 60% reduction in the fraction of CD4(+) cells expressing T1/ST2, a putative Th2 marker, and a parallel increase in the proportion expressing intracellular IFN-gamma following IP-10 treatment. The effect of IP-10 expression at the time of initial Ag encounter is persistent, as mice rechallenged with OVA following the resolution of acute inflammation exhibited reduced eosinophilia and IL-4 in the BAL. Collectively, these data illustrate that local expression of the chemokine IP-10 can introduce Th1 phenomena to a Th2-predisposed context and subvert the development of a Th2 response.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".