Dispersal of influenza virus populations within the respiratory tract shapes their evolutionary potential
Bibliographic record
Abstract
Viral infections are characterized by dispersal from an initial site to secondary locations within the host. How the resultant spatial heterogeneity shapes within-host genetic diversity and viral evolutionary pathways is poorly understood. Here, we show that virus dispersal within and between the nasal cavity and trachea maintains diversity and is therefore conducive to adaptive evolution, whereas dispersal to the lungs gives rise to population heterogeneity. We infected ferrets either intranasally or by aerosol with a barcoded influenza A/California/07/2009 (H1N1) virus. At 1, 2, or 4 days postinfection, dispersal was assessed by collecting 52 samples from throughout the respiratory tract of each animal. Irrespective of inoculation route, barcode compositions across the nasal turbinates and trachea were similar and highly diverse, revealing little constraint on the establishment of infection in the nasal cavity and descent through the trachea. Conversely, infection of the lungs produced genetically distinct viral populations. Lung populations were pauci-clonal, suggesting that each seeded location received relatively few viral genotypes. While aerosol inoculation gave distinct populations at every lung site sampled, within-host dispersal after intranasal inoculation produced larger patches, indicative of local expansion following seeding of the lungs. Throughout the respiratory tract, barcode diversity declined over time, but new diversity was generated through mutation. De novo variants were often unique to a given location, indicating that localized replication following dispersal resulted in population divergence. In summary, dispersal within the respiratory tract operates differently between regions and contributes to the potential for viral evolution to proceed independently in multiple within-host subpopulations.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".