Mapping of Clinical and Expression Quantitative Trait Loci in a Sex-Dependent Effect of Host Susceptibility to Mouse-Adapted Influenza H3N2/HK/1/68
Bibliographic record
Abstract
Seasonal influenza outbreaks and recurrent influenza pandemics present major challenges to public health. By studying immunological responses to influenza in different host species, it may be possible to discover common mechanisms of susceptibility in response to various influenza strains. This could lead to novel therapeutic targets with wide clinical application. Using a mouse-adapted strain of influenza (A/HK/1/68-MA20 [H3N2]), we produced a mouse model of severe influenza that reproduces the hallmark high viral load and overexpression of cytokines associated with susceptibility to severe influenza in humans. We mapped genetic determinants of the host response using a panel of 29 closely related mouse strains (AcB/BcA panel of recombinant congenic strains) created from influenza-susceptible A/J and influenza-resistant C57BL/6J (B6) mice. Combined clinical quantitative trait loci (QTL) and lung expression QTL mapping identified candidate genes for two sex-specific QTL on chromosomes 2 and 17. The former includes the previously described Hc gene, a deficit of which is associated with the susceptibility phenotype in females. The latter includes the phospholipase gene Pla2g7 and Tnfrsf21, a member of the TNFR superfamily. Confirmation of the gene underlying the chromosome 17 QTL may reveal new strategies for influenza treatment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".