Shifting of Immune Responsiveness to House Dust Mite by Influenza A Infection: Genomic Insights
Bibliographic record
Abstract
Respiratory viral infections have been associated with an increased incidence of allergic asthma. However, the mechanisms by which respiratory infections facilitate allergic airway disease are incompletely understood. We previously showed that exposure to a low dose of house dust mite (HDM) resulted in enhanced HDM-mediated allergic airway inflammation, and, importantly, marked airway hyperreactivity only when allergen exposure occurred during an acute influenza A infection. In this study, we evaluated the impact of concurrent influenza infection and allergen exposure at the genomic level, using whole-genome microarray. Our data showed that, in contrast to exposure to a low dose of HDM, influenza A infection led to a dramatic increase in gene expression, particularly of TLRs, C-type lectin receptors, several complement components, as well as FcεR1. Additionally, we observed increased expression of a number of genes encoding chemokines and cytokines associated with the recruitment of proinflammatory cells. Moreover, HDM exposure in the context of an influenza A infection resulted in the induction of unique genes, including calgranulin A (S100a8), an endogenous damage-associated molecular pattern and TLR4 agonist. In addition, we observed significantly increased expression of serum amyloid A (Saa3) and serine protease inhibitor 3n (Serpina3n). This study showed that influenza infection markedly increased the expression of multiple gene classes capable of sensing allergens and amplifying the ensuing immune-inflammatory response. We propose that influenza A infection primes the lung environment in such a way as to lower the threshold of allergen responsiveness, thus facilitating the emergence of a clinically significant allergic phenotype.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".