Convergent Evolution of Human-Isolated H7N9 Avian Influenza A Viruses
Bibliographic record
Abstract
Background: Avian influenza A virus H7N9 has caused 5 epidemic waves of human infections in China since 2013. Avian influenza A viruses may face strong selection to adapt to novel conditions when establishing themselves in humans. In this study, we sought to determine whether adaptive evolution had occurred in human-isolated H7N9 viruses. Methods: We evaluated all available genomes of H7N9 avian influenza A virus. Maximum likelihood trees were separately reconstructed for all 8 genes. Signals of positive selection and convergent evolution were then detected on branches that lead to changes in host tropism (from avian to human). Results: We found that 3 genes had significant signals of positive selection (all of them P < .05). In addition, we detected 34 sites having significant signals for parallel evolution in 8 genes (all of them P < .05), including 7 well-known sites (Q591K, E627K, and D701N in PB2 gene; R156K, V202A, and L244Q in HA; and R289K in NA) that play roles in crossing species barriers for avian influenza A viruses. Conclusion: Our study suggests that, during infection in humans, H7N9 viruses have undergone adaptive evolution to adapt to their new host environment and that the sites where parallel evolution occurred might play roles in crossing species barriers and respond to the new selection pressures arising from their new host environments.
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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 | 0.001 | 0.001 |
| 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".