Epithelial Memory After Respiratory Viral Infection in Mice Results in Prolonged Enhancement of Antigen Presentation
Bibliographic record
Abstract
BACKGROUND: Viral lower respiratory tract infections (LRTIs) can reduce the severity of subsequent LRTIs but have also been linked to respiratory allergy development and exacerbation. Here, we show that viral LRTI can imprint lung epithelial cells (LECs), leading to prolonged phenotypic and functional changes. METHODS: Mice were infected via intranasal administration of respiratory syncytial virus (RSV). After 28 days, LECs were isolated using cold dispase digestion followed by magnetic-activated cell sorting. Epigenetic changes were assessed using Cleavage Under Targets and Release Using Nuclease (CUT&RUN), while transcriptional changes were evaluated using NanoString and qPCR. Flow cytometry was employed to measure cell surface major histocompatibility complex (MHC) levels, antigen uptake and processing rates, and OT-I cell proliferation after antigen presentation. RESULTS: We identified epigenetic and transcriptomic changes in murine LECs 28 days after RSV infection, especially impacting genes associated with MHC. Lasting upregulation of MHC-I and MHC-II was further increased following in vivo LPS stimulation. Importantly, MHC upregulation was associated with increased antigen uptake and processing, as well as increased antigen presentation to T cells. CONCLUSIONS: Our data demonstrate that RSV can induce prolonged upregulation of antigen presentation by LECs, with the potential to facilitate local T cell responses to microbial antigens and allergens and to enhance immunity or in susceptible hosts respiratory allergy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".