Comparative Genomics of the Waterfowl Innate Immune System
Bibliographic record
Abstract
Animal species differ considerably in their ability to fight off infections. Finding the genetic basis of these differences is not easy, as the immune response is comprised of a complex network of proteins that interact with one another to defend the body against infection. Here, we used population- and comparative genomics to study the evolutionary forces acting on the innate immune system in natural hosts of the avian influenza virus (AIV). For this purpose, we used a combination of hybrid capture, next- generation sequencing and published genomes to examine genetic diversity, divergence, and signatures of selection in 127 innate immune genes at a micro- and macroevolutionary time scale in 26 species of waterfowl. We show across multiple immune pathways (AIV-, toll-like-, and RIG-I -like receptors signalling pathways) that genes involved genes in pathogen detection (i.e., toll-like receptors) and direct pathogen inhibition (i.e., antimicrobial peptides and interferon-stimulated genes), as well as host proteins targeted by viral antagonist proteins (i.e., mitochondrial antiviral-signaling protein, [MAVS]) are more likely to be polymorphic, genetically divergent, and under positive selection than other innate immune genes. Our results demonstrate that selective forces vary across innate immune signaling signalling pathways in waterfowl, and we present candidate genes that may contribute to differences in susceptibility and resistance to infectious diseases in wild birds, and that may be manipulated by viruses. Our findings improve our understanding of the interplay between host genetics and pathogens, and offer the opportunity for new insights into pathogenesis and potential drug targets.
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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) | 0.000 | 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".