The lung cytokine microenvironment influences molecular events in the lymph nodes during Th1 and Th2 respiratory mucosal sensitization to antigen<i>in vivo</i>
Bibliographic record
Abstract
Originally defined by their patterns of cytokine production, Th1 and Th2 cells have been described more recently to express other genes differentially as well, at least in vitro. In this study we compared the expression of Th1- and Th2-associated genes directly during in vivo sensitization to ovalbumin (OVA) in Th1- and Th2-polarized models of airways inflammation. Th1-polarized airway inflammation was achieved by the intranasal instillation of adenoviral vectors (Ad) encoding granulocyte-macrophage colony-stimulating factor (GM-CSF) and interleukin (IL)-12, followed by daily aerosolizations of OVA; instillation of Ad/GM-CSF alone with OVA aerosolization led to Th2-polarized responses. Lymph nodes were obtained at various time-points, RNA extracted, and analysed by real-time quantitative polymerase chain reaction (PCR). Consistent with reports from in vitro and human studies, mice undergoing Th1-polarized inflammation showed preferential expression of the transcription factor t-bet, the chemokines IFN-gamma inducible protein (IP)-10 and macrophage inflammatory protein 1 alpha (MIP-1-alpha), and the chemokine receptor CCR5. In contrast, the transcription factor GATA-3, the chemokines I-309 and thymus and activation regulated chemokine (TARC), and the chemokine receptors CCR3 and CCR4 were preferentially expressed in the Th2 model. Importantly, we also show that Ad/transgene expression remains compartmentalized to the lung after intranasal instillation. Flow cytometric analysis of lung myeloid dendritic cells indicated that B7.1 was expressed more strongly in the Th1 model than in the Th2 model. These studies provide a direct comparison of gene expression in in vivo Th1- and Th2-polarized models, and demonstrate that molecular events in the lymph nodes can be altered fundamentally by cytokine expression at distant mucosal sites.
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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 | 0.001 |
| 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".