The Differential Evolutionary Dynamics of Avian Cytokine and TLR Gene Classes
Bibliographic record
Abstract
The potential for investigating immune gene diversity has been greatly enhanced by recent advances in sequencing power. In this study, variation at two categories of avian immune genes with differing functional roles, pathogen detection and mediation of immune mechanisms, was examined using high-throughput sequencing. TLRs identify and alert the immune system by detecting molecular motifs that are conserved among pathogenic microorganisms, whereas cytokines act as mediators of resulting inflammation and immunity. Nine genes from each class were resequenced in a panel of domestic chickens and wild jungle fowl (JF). Tests on population-wide genetic variation between the gene classes indicated that allele frequency spectra at each group were distinctive. TLRs showed evidence pointing toward directional selection, whereas cytokines had signals more suggestive of frequency-dependent selection. This difference persisted between the distributions considering only coding sites, suggesting functional relevance. The unique patterns of variation at each gene class may be constrained by their different functional roles in the immune response. TLRs identify a relatively limited number of exogeneous pathogenic-related patterns and would be required to adapt quickly in response to evolving novel microbes encountered in new environmental niches. In contrast, cytokines interact with many molecules in mediating the power of immune mechanisms, and accordingly respond to the selective stimuli of many infectious diseases. Analyses also indicated that a general pattern of high variability has been enhanced by widespread genetic exchange between chicken and red JF, and possibly between chicken and gray JF at TLR1LA and TLR2A.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".