PB2 and Hemagglutinin Mutations Are Major Determinants of Host Range and Virulence in Mouse-Adapted Influenza A Virus
Bibliographic record
Abstract
Serial mouse lung passage of a human influenza A virus, A/Hong Kong/1/68 (H3N2) (HK-wt), produced a mouse-adapted variant, MA, with nine mutations that was >10(3.8)-fold more virulent. In this study, we demonstrate that MA mutations of the PB2 (D701N) and hemagglutinin (HA) (G218W in HA1 and T156N in HA2) genes were the most adaptive genetic determinants for increased growth and virulence in the mouse model. Recombinant viruses expressing each of the mutated MA genome segments on the HK-wt backbone showed significantly increased disease severity, whereas only the mouse-adapted PB2 gene increased virulence, as determined by the 50% lethal dose ([LD(50)] >10(1.4)-fold). The converse comparisons of recombinant MA viruses expressing each of the HK-wt genome segments showed the greatest decrease in virulence due to the HA gene (10(2)-fold), with lesser decreases due to the M1, NS1, NA, and PB1 genes (10(0.3)- to 10(0.8)-fold), and undetectable effects on the LD(50) for the PB2 and NP genes. The HK PB2 gene did, however, attenuate MA infection, as measured by weight loss and time to death. Replication of adaptive mutations in vivo and in vitro showed both viral gene backbone and host range effects. Minigenome transcription assays showed that PB1 and PB2 mutations increased polymerase activity and that the PB2 D701N mutation was comparable in effect to the mammalian adaptive PB2 E627K mutation. Our results demonstrate that host range and virulence are controlled by multiple genes, with major roles for mutations in PB2 and HA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".