The 2009 Pandemic H1N1 D222G Hemagglutinin Mutation Alters Receptor Specificity and Increases Virulence in Mice but Not in Ferrets
Bibliographic record
Abstract
BACKGROUND: The D222G (H1 numbering) hemagglutinin (HA) mutation within the receptor-binding site was detected with higher frequencies in severe cases of 2009 pandemic H1N1 (pH1N1) infections. We investigated the impact of this mutation in vitro and in animal models using recombinant pH1N1 viruses. METHODS: The recombinant D222G HA mutant was generated from a wild-type (WT) clinical strain by using reverse genetics and site-directed mutagenesis. Replicative capacities were determined in MDCK and MDCK-α2,6 cells. Antigenicity was characterized by HA inhibition and microneutralization assays. HA titers were determined using human, chicken, and resialylated turkey red blood cells (RBCs). Virulence and contact-transmissibility were analyzed in mice and ferrets. RESULTS: The recombinant D222G virus grew to significantly higher titers and generated larger viral plaques compared with the WT in MDCK but not in MDCK-α2,6 cells. The mutant also showed a significant reduction in HA titers using α2,6-expressing RBCs. The 2 recombinants were antigenically similar. The D222G mutant virus induced higher lung viral titers and alveolar inflammation in mice whereas the 2 recombinants had similar impacts in ferrets. CONCLUSIONS: The D222G HA mutation alters receptor binding specificity, resulting in higher lung titers in mice. This could contribute to the higher case fatality rates reported in humans.
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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) | 0.002 | 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".