Trained immunity induced by in vivo peptide-based STAT6 inhibition prevents ragweed allergy in mice
Bibliographic record
Abstract
BACKGROUND: Trained immunity is the ability of the innate immune system to form immune memory responses to provide support the formation of appropriate adaptive responses. Allergic airways disease (AAD) is a maladapted immune response to allergens, initiated and maintained by the type 2 (T2) inflammatory pathway. It is predicated by the elaboration of cytokines IL-4 and IL-13 and follows activation of the STAT6 transcription factor. OBJECTIVE: To investigate the role of trained immunity in mucosal immune responses following neonatal vaccination with the STAT6 inhibitory peptide (STAT6-IP), in preventing the development of ragweed-induced AAD. METHODS: T cells or dendritic cells (DC) from STAT6-IP vaccinated wild-type BALB/c mice to naïve mice, that were subsequently chronically exposed to sensitizing doses of ragweed allergen, is sufficient to prevent development of T2 responses in recipients. RESULTS: Our results demonstrate significant reductions in; airways hyperresponsiveness (AHR); ragweed-specific IgE; pulmonary inflammation; T2 cytokines; and inflammatory gene expressions in recipient mice. Expression of IDO, TGFβ and T regulatory cells were all significantly increased. Anti-TGFβ treatment during the ragweed sensitization phase re-constituted the pro-inflammatory T2 immune response. We show that tolerance can be attained via DC trained in the STAT6-IP-mediated tolerant milieu. This effect is not restricted to a particular allergen and does not require antigen-mediated T cell activation prior to transfer. CONCLUSION: Adoptive transfer experiments suggest that STAT6-IP treatment trains dendritic and cells to mediate tolerant immunity to chronic ragweed exposure in the airways. This indicates that early transient STAT6-inhibition constitutes an effective immunomodulatory airways allergy preventative strategy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".