Definition of the Cattle Killer Cell Ig–like Receptor Gene Family: Comparison with Aurochs and Human Counterparts
Bibliographic record
Abstract
Under selection pressure from pathogens, variable NK cell receptors that recognize polymorphic MHC class I evolved convergently in different species of placental mammal. Unexpectedly, diversified killer cell Ig-like receptors (KIRs) are shared by simian primates, including humans, and cattle, but not by other species. Whereas much is known of human KIR genetics and genomics, knowledge of cattle KIR is limited to nine cDNA sequences. To facilitate comparison of the cattle and human KIR gene families, we determined the genomic location, structure, and sequence of two cattle KIR haplotypes and defined KIR sequences of aurochs, the extinct wild ancestor of domestic cattle. Larger than its human counterpart, the cattle KIR locus evolved through successive duplications of a block containing ancestral KIR3DL and KIR3DX genes that existed before placental mammals. Comparison of two cattle KIR haplotypes and aurochs KIR show the KIR are polymorphic and the gene organization and content appear conserved. Of 18 genes, 8 are functional and 10 were inactivated by point mutation. Selective inactivation of KIR3DL and activating receptor genes leaves a functional cohort of one inhibitory KIR3DL, one activating KIR3DX, and six inhibitory KIR3DX. Functional KIR diversity evolved from KIR3DX in cattle and from KIR3DL in simian primates. Although independently evolved, cattle and human KIR gene families share important function-related properties, indicating that cattle KIR are NK cell receptors for cattle MHC class I. Combinations of KIR and MHC class I are the major genetic factors associated with human disease and merit investigation in cattle.
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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 | 0.001 |
| 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".