Host genotype and sex shape influenza evolution and defective viral genomes
Bibliographic record
Abstract
Abstract Viral evolution during initial pandemic waves favors mutations that enhance replication and transmission over antigenic escape. Host genotype and sex strongly shape this early adaptation, yet their individual and combined effects remain unclear. We experimentally adapted influenza A virus to male and female BALB/c and C57BL/6 mice, generating 28 independent lineages, and employed a novel “rolling sphere” approach to identify mutational hotspots in three-dimensional protein structures. In BALB/c mice, adaptation favored nonsynonymous substitutions linked to increased virulence, including a hemagglutinin variant exclusively fixed in female lineages. It also revealed the first demonstration of sex-dependent selection shaping a viral protein interface. In female-adapted viruses, substitutions disrupting a key NS1 dimerization motif converged on a single residue, while in male-adapted viruses, they were dispersed across the same interface. Conversely, adaptation to C57BL/6 resulted in fewer substitutions but promoted defective viral genome formation, leading to reduced cytopathic effect and attenuated virulence. This provides the first in vivo evidence that host genotype alone can modulate defective viral genome formation. Our results offer critical insights into host–pathogen interactions and reveal that selective pressures imposed by specific genotype–sex combinations can increase virulence across host genotypes, enabling new epidemiological modeling and disease control strategies.
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) | 0.000 | 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) | 0.002 | 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".