E119D Neuraminidase Mutation Conferring Pan-Resistance to Neuraminidase Inhibitors in an A(H1N1)pdm09 Isolate From a Stem-Cell Transplant Recipient
Bibliographic record
Abstract
BACKGROUND: An influenza A(H1N1)pdm09 infection was diagnosed in a hematopoietic stem cell transplant recipient during conditioning regimen. He was treated with oral oseltamivir, later combined with intravenous zanamivir. The H275Y neuraminidase (NA) mutation was first detected, and an E119D NA mutation was identified during zanamivir therapy. METHODS: Recombinant wild-type (WT) E119D and E119D/H275Y A(H1N1)pdm09 NA variants were generated by reverse genetics. Susceptibility to NA inhibitors (NAIs) was evaluated with a fluorometric assay using the 2'-(4-methylumbelliferyl)-α-D-N-acetylneuraminic acid (MUNANA) substrate. Susceptibility to favipiravir (T-705) was assessed using plaque reduction assays. The NA affinity and velocity values were determined with NA enzymatic studies. RESULTS: We identified an influenza A(H1N1)pdm09 E119D mutant that exhibited a marked increase in the 50% inhibitory concentrations against all tested NAIs (827-, 25-, 286-, and 702-fold for zanamivir, oseltamivir, peramivir, and laninamivir, respectively). The double E119D/H275Y mutation further increased oseltamivir and peramivir 50% inhibitory concentrations by 790- and >5000-fold, respectively, compared with the WT. The mutant viruses remained susceptible to favipiravir. The NA affinity and velocity values of the E119D variant decreased by 8.1-fold and 4.5-fold, respectively, compared with the WT. CONCLUSIONS: The actual emergence of a single NA mutation conferring pan-NAI resistance in the clinical setting reinforces the pressing need to develop new anti-influenza strategies.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".