Antigenic Drift of Influenza A(H7N9) Virus Hemagglutinin
Bibliographic record
Abstract
Background: Since the emergence of influenza A(H7N9) virus in 2013, there have been 5 waves of influenza A(H7N9) epidemics in China. However, evolution of the hemagglutinin (HA) protein antigenicity has not been systematically investigated. Methods: To better understand how antigenic drift in HA proteins of influenza (A)H7N9 virus occurs, 902 influenza A(H7N9) virus HA protein sequences from a public database were retrieved and analyzed. Fifty-three mutants with single amino acid substitutions in HA protein were introduced into pseudoviruses, and their antigenic characteristics were analyzed using pseudovirus-based assays. Results: The frequencies of 9 mutations incrementally increased over the past 5 years, with mutations identified at multiple sites. While mean neutralization titers of most variants remained unchanged, 3 mutations, A143V, A143T, and R148K, displayed a median 4-fold lower susceptibility to neutralization by antisera against influenza A/Anhui/1/2013(H7N9) virus. Notably, A143V and A143T were located outside the previously reported antigenic sites. The most dominant variant (A143V/R148K) in the most recent season constituted 74.11% of all mutations and demonstrated a 10-fold reduction in its reactivity to influenza A/Anhui/1/2013(H7N9) virus antisera. Importantly, compared with the DNA construct without the corresponding HA protein mutation, DNA vaccine encoding the A143V/R148K mutant induced a 5-fold increase in the neutralizing activity against this circulating virus. Conclusions: An appropriate vaccine strain should be considered in response to increasing antigenic drift in influenza A(H7N9) virus HA protein.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".