Characterization of cellular transcriptomic signatures induced by different respiratory viruses in human reconstituted airway epithelia
Bibliographic record
Abstract
Acute respiratory infections, a large part being of viral origin, constitute a major public health issue. To propose alternative and/or new therapeutic approaches, it is necessary to increase our knowledge about the interactions between respiratory viruses and their primary cellular targets using the most biologically relevant experimental models. In this study, we used RNAseq to characterize and compare the transcriptomic signature of infection induced by different major respiratory viruses (Influenza viruses, hRSV and hMPV) in a model of reconstituted human airway epithelia. Our results confirm the importance of several cellular pathways commonly or specifically induced by these respiratory viruses, such as the innate immune response or antiviral defense. A very interesting common feature revealed by the global virogenomic signature shared between hRSV, hMPV and influenza viruses is the global downregulation of cilium-related gene expression, in good agreement with experimental evaluation of mucociliary clearance. Beyond providing new information about respiratory virus/host interactions, our study also underlines the interest of using biologically relevant experimental models to study human respiratory viruses.
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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".