Intrahost Dynamics of Antiviral Resistance in Influenza A Virus Reflect Complex Patterns of Segment Linkage, Reassortment, and Natural Selection
Bibliographic record
Abstract
UNLABELLED: Resistance following antiviral therapy is commonly observed in human influenza viruses. Although this evolutionary process is initiated within individual hosts, little is known about the pattern, dynamics, and drivers of antiviral resistance at this scale, including the role played by reassortment. In addition, the short duration of human influenza virus infections limits the available time window in which to examine intrahost evolution. Using single-molecule sequencing, we mapped, in detail, the mutational spectrum of an H3N2 influenza A virus population sampled from an immunocompromised patient who shed virus over a 21-month period. In this unique natural experiment, we were able to document the complex dynamics underlying the evolution of antiviral resistance. Individual resistance mutations appeared weeks before they became dominant, evolved independently on cocirculating lineages, led to a genome-wide reduction in genetic diversity through a selective sweep, and were placed into new combinations by reassortment. Notably, despite frequent reassortment, phylogenetic analysis also provided evidence for specific patterns of segment linkage, with a strong association between the hemagglutinin (HA)- and matrix (M)-encoding segments that matches that previously observed at the epidemiological scale. In sum, we were able to reveal, for the first time, the complex interaction between multiple evolutionary processes as they occur within an individual host. IMPORTANCE: Understanding the evolutionary forces that shape the genetic diversity of influenza virus is crucial for predicting the emergence of drug-resistant strains but remains challenging because multiple processes occur concurrently. We characterized the evolution of antiviral resistance in a single persistent influenza virus infection, representing the first case in which reassortment and the complex patterns of drug resistance emergence and evolution have been determined within an individual host. Deep-sequence data from multiple time points revealed that the evolution of antiviral resistance reflects a combination of frequent mutation, natural selection, and a complex pattern of segment linkage and reassortment. In sum, these data show how immunocompromised hosts may help reveal the drivers of strain emergence.
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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 | 0.001 |
| 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".