Infectivity phenotypes of H3N2 influenza A viruses in primary swine respiratory epithelial cells are controlled by sialic acid binding
Bibliographic record
Abstract
BACKGROUND: In the late 1990s, triple reassortant H3N2 influenza A viruses emerged and spread widely in the US swine population. We have shown previously that an isolate representative of this virus-lineage, A/Swine/Minnesota/593/99 (Sw/MN), exhibits phenotypic differences compared to a wholly human-lineage H3N2 virus isolated during the same time period, A/Swine/Ontario/00130/97 (Sw/ONT). Specifically, Sw/MN was more infectious for pigs and infected a significantly higher proportion of cultured primary swine respiratory epithelial cells (SRECs). In addition, reverse genetics-generated Sw/MN × Sw/ONT reassortant and point mutant viruses demonstrated that the infectivity phenotypes in SRECs were strongly dependent on three amino acids within the hemagglutinin (HA) gene. OBJECTIVES: To determine the mechanism by which Sw/MN attains higher infectivity than Sw/ONT in SRECs. METHODS: A/Swine/Minnesota/593/99, Sw/ONT, and mutant (reverse genetics-generated HA reassortant and point mutant) viruses were compared at various HA-mediated stages of infection: initial sialic acid binding, virus entry, and the pH of virus-endosome fusion. RESULTS/CONCLUSIONS: Sialic acid binding was the sole stage where virus differences directly paralleled infectivity phenotypes in SRECs, indicating that binding is the primary mechanism responsible for differences in the infectivity levels of Sw/MN and Sw/ONT.
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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".