ICOS-Expressing CD4 T Cells Induced via TLR4 in the Nasal Mucosa Are Capable of Inhibiting Experimental Allergic Asthma
Bibliographic record
Abstract
Modulation of adaptive immune responses via the innate immune pattern recognition receptors, such as the TLRs, is an emerging strategy for vaccine development. We investigated whether nasal rather than intrapulmonary application of Protollin, a mucosal adjuvant composed of TLR2 and TLR4 ligands, is sufficient to elicit protection against murine allergic lower airway disease. Wild-type, Tlr2(-/-), or Tlr4(-/-) BALB/c mice were sensitized to a birch pollen allergen extract (BPEx), then received either intranasal or intrapulmonary administrations of Protollin or Protollin admixed with BPEx, followed by consecutive daily BPEx challenges. Nasal application of Protollin or Protollin admixed with BPEx was sufficient to inhibit allergic lower airway disease with minimal collateral lung inflammation. Inhibition was dependent on TLR4 and was associated with the induction of ICOS in cells of the nasal mucosa and on both CD4+Foxp3+ and CD4+Foxp3- T cells of the draining lymph nodes (LNs), as well as their recruitment to the lungs. Adoptive transfer of cervical LN CD4+ICOS+, but not CD4+ICOS-, cells inhibited BPEx-induced airway hyperresponsiveness and bronchoalveolar lavage eosinophilia. Thus, our data indicate that expansion of resident ICOS-expressing CD4+ T cells of the cervical LNs by nasal mucosal TLR4 stimulation may inhibit the development of allergic lower airway disease in mice.
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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 | 0.000 |
| 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 | 0.001 |
| 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".